{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "请点击[此处](https://ai.baidu.com/docs#/AIStudio_Project_Notebook/a38e5576)查看本环境基本用法.  <br>\n",
    "Please click [here ](https://ai.baidu.com/docs#/AIStudio_Project_Notebook/a38e5576) for more detailed instructions. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# AI达人创造营第二期--基于PaddleX的安全帽检测  \n",
    "**作业四：撰写项目README并完成开源**\n",
    "\n",
    "## 评分标准\n",
    "1.格式规范（有至少3个小标题，内容完整），一个小标题5分，最高20分\n",
    "\n",
    "2.图文并茂，一张图5分，最高20分\n",
    "\n",
    "3.有可运行的代码，且代码内有详细注释，20分\n",
    "\n",
    "4.代码开源到github，15分\n",
    "\n",
    "5.代码同步到gitee，5分\n",
    "\n",
    "## 作业目的\n",
    "使用MarkDown撰写项目并且学会使用开源工具。\n",
    "\n",
    "\n",
    "\n",
    "## 参考资料：\n",
    "- [如何写好一篇高质量的精选项目？](https://aistudio.baidu.com/aistudio/projectdetail/2175889)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 一、项目背景介绍\n",
    "在施工现场，对于来往人员，以及工作人员而言，安全问题至关重要。而安全帽更是保障施工现场在场人员安全的第一防线，因此需要对场地中的人员进行安全提醒。当人员未佩戴安全帽进入施工场所时，人为监管耗时耗力，而且不易实时监管，过程繁琐、消耗人力且实时性较差。针对上述问题，希望通过视频监控->目标检测->智能督导的方式智能、高效的完成此任务:  \n",
    "\n",
    "<center><img src=\"https://ai-studio-static-online.cdn.bcebos.com/63b901ca44ca482abb31511b8b99faed4cdbad7d9d7c467e8cdd169181895bb4\" width = \"500\"></center>\n",
    "<center><br>图1：安全施工图 </br></center>\n",
    "<br></br>\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 二、数据处理\n",
    "**2.1PaddleX简介：**  \n",
    "PaddleX是飞桨全流程开发工具，集飞桨核心框架、模型库、工具及组件等深度学习开发所需全部能力于一身，打通深度学习开发全流程，并**提供简明易懂的Python API**，方便用户根据实际生产需求进行直接调用或二次开发，为开发者提供飞桨全流程开发的最佳实践。目前，该工具代码已开源于GitHub，同时可访问PaddleX在线使用文档，快速查阅读使用教程和API文档说明。  \n",
    "[PaddleX代码GitHub链接](https://github.com/PaddlePaddle/PaddleX/tree/develop)  \n",
    "[PaddleX文档链接](https://paddlex.readthedocs.io/zh_CN/develop/index.html)  \n",
    "**2.2安装PaddleX**  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false,
    "execution": {
     "iopub.execute_input": "2022-02-22T08:15:50.492945Z",
     "iopub.status.busy": "2022-02-22T08:15:50.491958Z",
     "iopub.status.idle": "2022-02-22T08:16:21.584870Z",
     "shell.execute_reply": "2022-02-22T08:16:21.584083Z",
     "shell.execute_reply.started": "2022-02-22T08:15:50.492906Z"
    },
    "jupyter": {
     "outputs_hidden": false
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "Requirement already satisfied: MarkupSafe>=0.23 in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from Jinja2>=2.10.1->flask>=1.1.1->visualdl>=2.2.2->paddlex) (2.0.1)\n",
      "Building wheels for collected packages: lap, pycocotools, py-cpuinfo\n",
      "  Building wheel for lap (setup.py) ... \u001b[?25ldone\n",
      "\u001b[?25h  Created wheel for lap: filename=lap-0.4.0-cp37-cp37m-linux_x86_64.whl size=1593866 sha256=af94c12c47da10a3f6d0d2e043729cb4e8d14790e03d0f595a59f03c1c0d62cc\n",
      "  Stored in directory: /home/aistudio/.cache/pip/wheels/95/5f/20/9e2b2cfb8b2bfae5a5374e947511a47c8909e74aaf6d6d4464\n",
      "  Building wheel for pycocotools (pyproject.toml) ... \u001b[?25ldone\n",
      "\u001b[?25h  Created wheel for pycocotools: filename=pycocotools-2.0.4-cp37-cp37m-linux_x86_64.whl size=273788 sha256=9087023f5a640858ed68729c7aa7dcda3c377325bdf57f084121bac89c8054d3\n",
      "  Stored in directory: /home/aistudio/.cache/pip/wheels/d0/74/13/98b11419a029f3c25590419747f1ec26f5494beae1d457560b\n",
      "  Building wheel for py-cpuinfo (setup.py) ... \u001b[?25ldone\n",
      "\u001b[?25h  Created wheel for py-cpuinfo: filename=py_cpuinfo-8.0.0-py3-none-any.whl size=22245 sha256=9696a6199a41cd32bf9f926280d0ffdf52c0781f81b09340ac22e91622350ee6\n",
      "  Stored in directory: /home/aistudio/.cache/pip/wheels/9c/57/dd/323247bc3b04fce7bc3fa4c25c106b87f2c13888c240b68723\n",
      "Successfully built lap pycocotools py-cpuinfo\n",
      "Installing collected packages: tomli, py, iniconfig, pytest, py-cpuinfo, xmltodict, pytest-benchmark, flake8-import-order, visualdl, shapely, scikit-learn, pycocotools, paddleslim, motmetrics, lap, paddlex\n",
      "  Attempting uninstall: visualdl\n",
      "    Found existing installation: visualdl 2.2.0\n",
      "    Uninstalling visualdl-2.2.0:\n",
      "      Successfully uninstalled visualdl-2.2.0\n",
      "  Attempting uninstall: scikit-learn\n",
      "    Found existing installation: scikit-learn 0.24.2\n",
      "    Uninstalling scikit-learn-0.24.2:\n",
      "      Successfully uninstalled scikit-learn-0.24.2\n",
      "Successfully installed flake8-import-order-0.18.1 iniconfig-1.1.1 lap-0.4.0 motmetrics-1.2.0 paddleslim-2.2.1 paddlex-2.1.0 py-1.11.0 py-cpuinfo-8.0.0 pycocotools-2.0.4 pytest-7.0.1 pytest-benchmark-3.4.1 scikit-learn-0.23.2 shapely-1.8.1.post1 tomli-2.0.1 visualdl-2.2.3 xmltodict-0.12.0\n",
      "\u001b[33mWARNING: You are using pip version 21.3.1; however, version 22.0.3 is available.\n",
      "You should consider upgrading via the '/opt/conda/envs/python35-paddle120-env/bin/python -m pip install --upgrade pip' command.\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "!pip install paddlex -i https://mirror.baidu.com/pypi/simple"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**2.3挂载数据集**\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false,
    "execution": {
     "iopub.execute_input": "2022-02-22T08:19:47.596719Z",
     "iopub.status.busy": "2022-02-22T08:19:47.596234Z",
     "iopub.status.idle": "2022-02-22T08:19:56.722703Z",
     "shell.execute_reply": "2022-02-22T08:19:56.721796Z",
     "shell.execute_reply.started": "2022-02-22T08:19:47.596678Z"
    },
    "jupyter": {
     "outputs_hidden": false
    },
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "!unzip -oq data/data50329/HelmetDetection.zip -d data/ #把所挂载的数据集archive.zip解压到data/Helmet文件夹目录下"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**2.4划分数据集**  \n",
    "\n",
    "需要在data文件夹下 对解压出来的 annotatios文件夹重命名成---Annotations\n",
    "images文件夹重命名成JPEGImages文件夹"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false,
    "execution": {
     "iopub.execute_input": "2022-02-22T08:21:10.816599Z",
     "iopub.status.busy": "2022-02-22T08:21:10.815122Z",
     "iopub.status.idle": "2022-02-22T08:21:17.539141Z",
     "shell.execute_reply": "2022-02-22T08:21:17.538373Z",
     "shell.execute_reply.started": "2022-02-22T08:21:10.816527Z"
    },
    "jupyter": {
     "outputs_hidden": false
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[32m[02-22 16:21:12 MainThread @logger.py:242]\u001b[0m Argv: /opt/conda/envs/python35-paddle120-env/bin/paddlex --split_dataset --format VOC --dataset_dir /home/aistudio/data/ --val_value 0.2 --test_value 0.1\n",
      "\u001b[0m\u001b[33m[02-22 16:21:12 MainThread @utils.py:79]\u001b[0m \u001b[5m\u001b[33mWRN\u001b[0m paddlepaddle version: 2.2.2. The dynamic graph version of PARL is under development, not fully tested and supported\n",
      "\u001b[0m/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/parl/remote/communication.py:38: DeprecationWarning: 'pyarrow.default_serialization_context' is deprecated as of 2.0.0 and will be removed in a future version. Use pickle or the pyarrow IPC functionality instead.\n",
      "  context = pyarrow.default_serialization_context()\n",
      "\u001b[0m/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/matplotlib/__init__.py:107: DeprecationWarning: Using or importing the ABCs from 'collections' instead of from 'collections.abc' is deprecated, and in 3.8 it will stop working\n",
      "  from collections import MutableMapping\n",
      "\u001b[0m/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/matplotlib/rcsetup.py:20: DeprecationWarning: Using or importing the ABCs from 'collections' instead of from 'collections.abc' is deprecated, and in 3.8 it will stop working\n",
      "  from collections import Iterable, Mapping\n",
      "\u001b[0m/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/matplotlib/colors.py:53: DeprecationWarning: Using or importing the ABCs from 'collections' instead of from 'collections.abc' is deprecated, and in 3.8 it will stop working\n",
      "  from collections import Sized\n",
      "2022-02-22 16:21:16 [INFO]\tDataset split starts...\u001b[0m\n",
      "\u001b[0m2022-02-22 16:21:16 [INFO]\tDataset split done.\u001b[0m\n",
      "\u001b[0m2022-02-22 16:21:16 [INFO]\tTrain samples: 3500\u001b[0m\n",
      "\u001b[0m2022-02-22 16:21:16 [INFO]\tEval samples: 1000\u001b[0m\n",
      "\u001b[0m2022-02-22 16:21:16 [INFO]\tTest samples: 500\u001b[0m\n",
      "\u001b[0m2022-02-22 16:21:16 [INFO]\tSplit files saved in /home/aistudio/data/\u001b[0m\n",
      "\u001b[0m\u001b[0m\u001b[0m"
     ]
    }
   ],
   "source": [
    "!paddlex --split_dataset --format VOC --dataset_dir /home/aistudio/data/ --val_value 0.2 --test_value 0.1#划分数据集"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 三、模型选择和调参\n",
    "**3.1 YOLOv3模型设计思想**\n",
    "\n",
    "YOLOv3算法的基本思想可以分成两部分：\n",
    "\n",
    "* 按一定规则在图片上产生一系列的候选区域，然后根据这些候选区域与图片上物体真实框之间的位置关系对候选区域进行标注。跟真实框足够接近的那些候选区域会被标注为正样本，同时将真实框的位置作为正样本的位置目标。偏离真实框较大的那些候选区域则会被标注为负样本，负样本不需要预测位置或者类别。\n",
    "* 使用卷积神经网络提取图片特征并对候选区域的位置和类别进行预测。这样每个预测框就可以看成是一个样本，根据真实框相对它的位置和类别进行了标注而获得标签值，通过网络模型预测其位置和类别，将网络预测值和标签值进行比较，就可以建立起损失函数。\n",
    "\n",
    "YOLOv3算法训练过程的流程图如 **图2** 所示：\n",
    "\n",
    "<br></br>\n",
    "<center><img src=\"https://ai-studio-static-online.cdn.bcebos.com/f2eb2b75bb5a4e518b86a257e0f931de7377dba3bba44d1e846b307036aed41a\" width = \"800\"></center>\n",
    "<center><br>图2：YOLOv3算法训练流程图 </br></center>\n",
    "<br></br>\n",
    "\n",
    "\n",
    "* **图2** 左边是输入图片，上半部分所示的过程是使用卷积神经网络对图片提取特征，随着网络不断向前传播，特征图的尺寸越来越小，每个像素点会代表更加抽象的特征模式，直到输出特征图，其尺寸减小为原图的$\\frac{1}{32}$。\n",
    "* **图2** 下半部分描述了生成候选区域的过程，首先将原图划分成多个小方块，每个小方块的大小是$32 \\times 32$，然后以每个小方块为中心分别生成一系列锚框，整张图片都会被锚框覆盖到。在每个锚框的基础上产生一个与之对应的预测框，根据锚框和预测框与图片上物体真实框之间的位置关系，对这些预测框进行标注。\n",
    "* 将上方支路中输出的特征图与下方支路中产生的预测框标签建立关联，创建损失函数，开启端到端的训练过程。\n",
    "\n",
    "这里我们直接用PaddleX调用YOLOv3模型"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**3.2配置GPU**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false,
    "execution": {
     "iopub.execute_input": "2022-02-22T08:26:29.733436Z",
     "iopub.status.busy": "2022-02-22T08:26:29.732548Z",
     "iopub.status.idle": "2022-02-22T08:26:34.182638Z",
     "shell.execute_reply": "2022-02-22T08:26:34.181937Z",
     "shell.execute_reply.started": "2022-02-22T08:26:29.733393Z"
    },
    "jupyter": {
     "outputs_hidden": false
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[02-22 16:26:31 MainThread @utils.py:79] WRN paddlepaddle version: 2.2.2. The dynamic graph version of PARL is under development, not fully tested and supported\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/parl/remote/communication.py:38: DeprecationWarning: 'pyarrow.default_serialization_context' is deprecated as of 2.0.0 and will be removed in a future version. Use pickle or the pyarrow IPC functionality instead.\n",
      "  context = pyarrow.default_serialization_context()\n"
     ]
    }
   ],
   "source": [
    "import matplotlib\n",
    "matplotlib.use('Agg') \n",
    "import os\n",
    "os.environ['CUDA_VISIBLE_DEVICES'] = '0'\n",
    "import paddlex as pdx"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**3.3 定义图像处理流程transforms**     \n",
    "定义数据处理流程，其中训练集和验证集需分别定义，训练过程包括了部分测试过程中不需要的数据增强操作，如在本示例中，训练过程使用了MixupImage、RandomDistort、RandomExpand、RandomCrop和RandomHorizontalFlip共5种数据增强方式，更多图像预处理流程transforms的使用可参见paddlex.det.transforms。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false,
    "execution": {
     "iopub.execute_input": "2022-02-22T08:26:36.625466Z",
     "iopub.status.busy": "2022-02-22T08:26:36.625067Z",
     "iopub.status.idle": "2022-02-22T08:26:36.631994Z",
     "shell.execute_reply": "2022-02-22T08:26:36.631387Z",
     "shell.execute_reply.started": "2022-02-22T08:26:36.625427Z"
    },
    "jupyter": {
     "outputs_hidden": false
    },
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "import paddlex as pdx\n",
    "from paddlex import transforms as T\n",
    "train_transforms = T.Compose([\n",
    "    T.MixupImage(mixup_epoch=250), T.RandomDistort(),\n",
    "    T.RandomExpand(im_padding_value=[123.675, 116.28, 103.53]), T.RandomCrop(),\n",
    "    T.RandomHorizontalFlip(), T.BatchRandomResize(\n",
    "        target_sizes=[320, 352, 384, 416, 448, 480, 512, 544, 576, 608],\n",
    "        interp='RANDOM'), T.Normalize(\n",
    "            mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])\n",
    "])\n",
    "\n",
    "eval_transforms = T.Compose([\n",
    "    T.Resize(\n",
    "        608, interp='CUBIC'), T.Normalize(\n",
    "            mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])\n",
    "])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**3.4 定义数据集Dataset**  \n",
    "目标检测可使用VOCDetection格式和COCODetection两种数据集，此处由于数据集为VOC格式，因此采用pdx.datasets.VOCDetection来加载数据集，该接口的介绍可参见文档paddlex.datasets.VOCDetection。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false,
    "execution": {
     "iopub.execute_input": "2022-02-22T08:26:43.169842Z",
     "iopub.status.busy": "2022-02-22T08:26:43.169293Z",
     "iopub.status.idle": "2022-02-22T08:26:55.682440Z",
     "shell.execute_reply": "2022-02-22T08:26:55.681822Z",
     "shell.execute_reply.started": "2022-02-22T08:26:43.169801Z"
    },
    "jupyter": {
     "outputs_hidden": false
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2022-02-22 16:26:43 [INFO]\tStarting to read file list from dataset...\n",
      "2022-02-22 16:26:52 [INFO]\t3500 samples in file data/train_list.txt, including 3500 positive samples and 0 negative samples.\n",
      "creating index...\n",
      "index created!\n",
      "2022-02-22 16:26:52 [INFO]\tStarting to read file list from dataset...\n",
      "2022-02-22 16:26:55 [INFO]\t1000 samples in file data/val_list.txt, including 1000 positive samples and 0 negative samples.\n",
      "creating index...\n",
      "index created!\n"
     ]
    }
   ],
   "source": [
    "train_dataset = pdx.datasets.VOCDetection(\n",
    "    data_dir='data',\n",
    "    file_list='data/train_list.txt',\n",
    "    label_list='data/labels.txt',\n",
    "    transforms=train_transforms,\n",
    "    shuffle=True)\n",
    "\n",
    "eval_dataset = pdx.datasets.VOCDetection(\n",
    "    data_dir='data',\n",
    "    file_list='data/val_list.txt',\n",
    "    label_list='data/labels.txt',\n",
    "    transforms=eval_transforms,\n",
    "    shuffle=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**2.4 模型选择**  \n",
    "使用YOLOv3模型，DarkNet53网络"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false,
    "execution": {
     "iopub.execute_input": "2022-02-22T08:27:00.420664Z",
     "iopub.status.busy": "2022-02-22T08:27:00.419633Z",
     "iopub.status.idle": "2022-02-22T08:27:03.509387Z",
     "shell.execute_reply": "2022-02-22T08:27:03.505820Z",
     "shell.execute_reply.started": "2022-02-22T08:27:00.420609Z"
    },
    "jupyter": {
     "outputs_hidden": false
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "W0222 16:27:00.423406   150 device_context.cc:447] Please NOTE: device: 0, GPU Compute Capability: 7.0, Driver API Version: 10.1, Runtime API Version: 10.1\n",
      "W0222 16:27:00.429230   150 device_context.cc:465] device: 0, cuDNN Version: 7.6.\n"
     ]
    }
   ],
   "source": [
    "num_classes = len(train_dataset.labels)\n",
    "model = pdx.det.YOLOv3(num_classes=num_classes, backbone='DarkNet53')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 四、模型训练\n",
    "**4.1配置超参数训练模型**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false,
    "execution": {
     "iopub.execute_input": "2022-02-22T08:27:05.507723Z",
     "iopub.status.busy": "2022-02-22T08:27:05.506781Z",
     "iopub.status.idle": "2022-02-22T09:36:18.945855Z",
     "shell.execute_reply": "2022-02-22T09:36:18.944944Z",
     "shell.execute_reply.started": "2022-02-22T08:27:05.507677Z"
    },
    "jupyter": {
     "outputs_hidden": false
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2022-02-22 16:27:05 [INFO]\tDownloading DarkNet53_pretrained.pdparams from https://paddledet.bj.bcebos.com/models/pretrained/DarkNet53_pretrained.pdparams\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 158704/158704 [00:06<00:00, 23558.69KB/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2022-02-22 16:27:12 [INFO]\tLoading pretrained model from output/yolov3_darknet53/pretrain/DarkNet53_pretrained.pdparams\n",
      "2022-02-22 16:27:12 [WARNING]\tneck.yolo_block.0.conv_module.conv0.conv.weight is not in pretrained model\n",
      "2022-02-22 16:27:12 [WARNING]\tneck.yolo_block.0.conv_module.conv0.batch_norm.weight is not in pretrained model\n",
      "2022-02-22 16:27:12 [WARNING]\tneck.yolo_block.0.conv_module.conv0.batch_norm.bias is not in pretrained model\n",
      "2022-02-22 16:27:12 [WARNING]\tneck.yolo_block.0.conv_module.conv0.batch_norm._mean is not in pretrained model\n",
      "2022-02-22 16:27:12 [WARNING]\tneck.yolo_block.0.conv_module.conv0.batch_norm._variance is not in pretrained model\n",
      "2022-02-22 16:27:12 [WARNING]\tneck.yolo_block.0.conv_module.conv1.conv.weight is not in pretrained model\n",
      "2022-02-22 16:27:12 [WARNING]\tneck.yolo_block.0.conv_module.conv1.batch_norm.weight is not in pretrained model\n",
      "2022-02-22 16:27:12 [WARNING]\tneck.yolo_block.0.conv_module.conv1.batch_norm.bias is not in pretrained model\n",
      "2022-02-22 16:27:12 [WARNING]\tneck.yolo_block.0.conv_module.conv1.batch_norm._mean is not in pretrained model\n",
      "2022-02-22 16:27:12 [WARNING]\tneck.yolo_block.0.conv_module.conv1.batch_norm._variance is not in pretrained model\n",
      "2022-02-22 16:27:12 [WARNING]\tneck.yolo_block.0.conv_module.conv2.conv.weight is not in pretrained model\n",
      "2022-02-22 16:27:12 [WARNING]\tneck.yolo_block.0.conv_module.conv2.batch_norm.weight is not in pretrained model\n",
      "2022-02-22 16:27:12 [WARNING]\tneck.yolo_block.0.conv_module.conv2.batch_norm.bias is not in pretrained model\n",
      "2022-02-22 16:27:12 [WARNING]\tneck.yolo_block.0.conv_module.conv2.batch_norm._mean is not in pretrained model\n",
      "2022-02-22 16:27:12 [WARNING]\tneck.yolo_block.0.conv_module.conv2.batch_norm._variance is not in pretrained model\n",
      "2022-02-22 16:27:12 [WARNING]\tneck.yolo_block.0.conv_module.conv3.conv.weight is not in pretrained model\n",
      "2022-02-22 16:27:12 [WARNING]\tneck.yolo_block.0.conv_module.conv3.batch_norm.weight is not in pretrained model\n",
      "2022-02-22 16:27:12 [WARNING]\tneck.yolo_block.0.conv_module.conv3.batch_norm.bias is not in pretrained model\n",
      "2022-02-22 16:27:12 [WARNING]\tneck.yolo_block.0.conv_module.conv3.batch_norm._mean is not in pretrained model\n",
      "2022-02-22 16:27:12 [WARNING]\tneck.yolo_block.0.conv_module.conv3.batch_norm._variance is not in pretrained model\n",
      "2022-02-22 16:27:12 [WARNING]\tneck.yolo_block.0.conv_module.route.conv.weight is not in pretrained model\n",
      "2022-02-22 16:27:12 [WARNING]\tneck.yolo_block.0.conv_module.route.batch_norm.weight is not in pretrained model\n",
      "2022-02-22 16:27:12 [WARNING]\tneck.yolo_block.0.conv_module.route.batch_norm.bias is not in pretrained model\n",
      "2022-02-22 16:27:12 [WARNING]\tneck.yolo_block.0.conv_module.route.batch_norm._mean is not in pretrained model\n",
      "2022-02-22 16:27:12 [WARNING]\tneck.yolo_block.0.conv_module.route.batch_norm._variance is not in pretrained model\n",
      "2022-02-22 16:27:12 [WARNING]\tneck.yolo_block.0.tip.conv.weight is not in pretrained model\n",
      "2022-02-22 16:27:12 [WARNING]\tneck.yolo_block.0.tip.batch_norm.weight is not in pretrained model\n",
      "2022-02-22 16:27:12 [WARNING]\tneck.yolo_block.0.tip.batch_norm.bias is not in pretrained model\n",
      "2022-02-22 16:27:12 [WARNING]\tneck.yolo_block.0.tip.batch_norm._mean is not in pretrained model\n",
      "2022-02-22 16:27:12 [WARNING]\tneck.yolo_block.0.tip.batch_norm._variance is not in pretrained model\n",
      "2022-02-22 16:27:12 [WARNING]\tneck.yolo_transition.0.conv.weight is not in pretrained model\n",
      "2022-02-22 16:27:12 [WARNING]\tneck.yolo_transition.0.batch_norm.weight is not in pretrained model\n",
      "2022-02-22 16:27:12 [WARNING]\tneck.yolo_transition.0.batch_norm.bias is not in pretrained model\n",
      "2022-02-22 16:27:12 [WARNING]\tneck.yolo_transition.0.batch_norm._mean is not in pretrained model\n",
      "2022-02-22 16:27:12 [WARNING]\tneck.yolo_transition.0.batch_norm._variance is not in pretrained model\n",
      "2022-02-22 16:27:12 [WARNING]\tneck.yolo_block.1.conv_module.conv0.conv.weight is not in pretrained model\n",
      "2022-02-22 16:27:12 [WARNING]\tneck.yolo_block.1.conv_module.conv0.batch_norm.weight is not in pretrained model\n",
      "2022-02-22 16:27:12 [WARNING]\tneck.yolo_block.1.conv_module.conv0.batch_norm.bias is not in pretrained model\n",
      "2022-02-22 16:27:12 [WARNING]\tneck.yolo_block.1.conv_module.conv0.batch_norm._mean is not in pretrained model\n",
      "2022-02-22 16:27:12 [WARNING]\tneck.yolo_block.1.conv_module.conv0.batch_norm._variance is not in pretrained model\n",
      "2022-02-22 16:27:12 [WARNING]\tneck.yolo_block.1.conv_module.conv1.conv.weight is not in pretrained model\n",
      "2022-02-22 16:27:12 [WARNING]\tneck.yolo_block.1.conv_module.conv1.batch_norm.weight is not in pretrained model\n",
      "2022-02-22 16:27:12 [WARNING]\tneck.yolo_block.1.conv_module.conv1.batch_norm.bias is not in pretrained model\n",
      "2022-02-22 16:27:12 [WARNING]\tneck.yolo_block.1.conv_module.conv1.batch_norm._mean is not in pretrained model\n",
      "2022-02-22 16:27:12 [WARNING]\tneck.yolo_block.1.conv_module.conv1.batch_norm._variance is not in pretrained model\n",
      "2022-02-22 16:27:12 [WARNING]\tneck.yolo_block.1.conv_module.conv2.conv.weight is not in pretrained model\n",
      "2022-02-22 16:27:12 [WARNING]\tneck.yolo_block.1.conv_module.conv2.batch_norm.weight is not in pretrained model\n",
      "2022-02-22 16:27:12 [WARNING]\tneck.yolo_block.1.conv_module.conv2.batch_norm.bias is not in pretrained model\n",
      "2022-02-22 16:27:12 [WARNING]\tneck.yolo_block.1.conv_module.conv2.batch_norm._mean is not in pretrained model\n",
      "2022-02-22 16:27:12 [WARNING]\tneck.yolo_block.1.conv_module.conv2.batch_norm._variance is not in pretrained model\n",
      "2022-02-22 16:27:12 [WARNING]\tneck.yolo_block.1.conv_module.conv3.conv.weight is not in pretrained model\n",
      "2022-02-22 16:27:12 [WARNING]\tneck.yolo_block.1.conv_module.conv3.batch_norm.weight is not in pretrained model\n",
      "2022-02-22 16:27:12 [WARNING]\tneck.yolo_block.1.conv_module.conv3.batch_norm.bias is not in pretrained model\n",
      "2022-02-22 16:27:12 [WARNING]\tneck.yolo_block.1.conv_module.conv3.batch_norm._mean is not in pretrained model\n",
      "2022-02-22 16:27:12 [WARNING]\tneck.yolo_block.1.conv_module.conv3.batch_norm._variance is not in pretrained model\n",
      "2022-02-22 16:27:12 [WARNING]\tneck.yolo_block.1.conv_module.route.conv.weight is not in pretrained model\n",
      "2022-02-22 16:27:12 [WARNING]\tneck.yolo_block.1.conv_module.route.batch_norm.weight is not in pretrained model\n",
      "2022-02-22 16:27:12 [WARNING]\tneck.yolo_block.1.conv_module.route.batch_norm.bias is not in pretrained model\n",
      "2022-02-22 16:27:12 [WARNING]\tneck.yolo_block.1.conv_module.route.batch_norm._mean is not in pretrained model\n",
      "2022-02-22 16:27:12 [WARNING]\tneck.yolo_block.1.conv_module.route.batch_norm._variance is not in pretrained model\n",
      "2022-02-22 16:27:12 [WARNING]\tneck.yolo_block.1.tip.conv.weight is not in pretrained model\n",
      "2022-02-22 16:27:12 [WARNING]\tneck.yolo_block.1.tip.batch_norm.weight is not in pretrained model\n",
      "2022-02-22 16:27:12 [WARNING]\tneck.yolo_block.1.tip.batch_norm.bias is not in pretrained model\n",
      "2022-02-22 16:27:12 [WARNING]\tneck.yolo_block.1.tip.batch_norm._mean is not in pretrained model\n",
      "2022-02-22 16:27:12 [WARNING]\tneck.yolo_block.1.tip.batch_norm._variance is not in pretrained model\n",
      "2022-02-22 16:27:12 [WARNING]\tneck.yolo_transition.1.conv.weight is not in pretrained model\n",
      "2022-02-22 16:27:12 [WARNING]\tneck.yolo_transition.1.batch_norm.weight is not in pretrained model\n",
      "2022-02-22 16:27:12 [WARNING]\tneck.yolo_transition.1.batch_norm.bias is not in pretrained model\n",
      "2022-02-22 16:27:12 [WARNING]\tneck.yolo_transition.1.batch_norm._mean is not in pretrained model\n",
      "2022-02-22 16:27:12 [WARNING]\tneck.yolo_transition.1.batch_norm._variance is not in pretrained model\n",
      "2022-02-22 16:27:12 [WARNING]\tneck.yolo_block.2.conv_module.conv0.conv.weight is not in pretrained model\n",
      "2022-02-22 16:27:12 [WARNING]\tneck.yolo_block.2.conv_module.conv0.batch_norm.weight is not in pretrained model\n",
      "2022-02-22 16:27:12 [WARNING]\tneck.yolo_block.2.conv_module.conv0.batch_norm.bias is not in pretrained model\n",
      "2022-02-22 16:27:12 [WARNING]\tneck.yolo_block.2.conv_module.conv0.batch_norm._mean is not in pretrained model\n",
      "2022-02-22 16:27:12 [WARNING]\tneck.yolo_block.2.conv_module.conv0.batch_norm._variance is not in pretrained model\n",
      "2022-02-22 16:27:12 [WARNING]\tneck.yolo_block.2.conv_module.conv1.conv.weight is not in pretrained model\n",
      "2022-02-22 16:27:12 [WARNING]\tneck.yolo_block.2.conv_module.conv1.batch_norm.weight is not in pretrained model\n",
      "2022-02-22 16:27:12 [WARNING]\tneck.yolo_block.2.conv_module.conv1.batch_norm.bias is not in pretrained model\n",
      "2022-02-22 16:27:12 [WARNING]\tneck.yolo_block.2.conv_module.conv1.batch_norm._mean is not in pretrained model\n",
      "2022-02-22 16:27:12 [WARNING]\tneck.yolo_block.2.conv_module.conv1.batch_norm._variance is not in pretrained model\n",
      "2022-02-22 16:27:12 [WARNING]\tneck.yolo_block.2.conv_module.conv2.conv.weight is not in pretrained model\n",
      "2022-02-22 16:27:12 [WARNING]\tneck.yolo_block.2.conv_module.conv2.batch_norm.weight is not in pretrained model\n",
      "2022-02-22 16:27:12 [WARNING]\tneck.yolo_block.2.conv_module.conv2.batch_norm.bias is not in pretrained model\n",
      "2022-02-22 16:27:12 [WARNING]\tneck.yolo_block.2.conv_module.conv2.batch_norm._mean is not in pretrained model\n",
      "2022-02-22 16:27:12 [WARNING]\tneck.yolo_block.2.conv_module.conv2.batch_norm._variance is not in pretrained model\n",
      "2022-02-22 16:27:12 [WARNING]\tneck.yolo_block.2.conv_module.conv3.conv.weight is not in pretrained model\n",
      "2022-02-22 16:27:12 [WARNING]\tneck.yolo_block.2.conv_module.conv3.batch_norm.weight is not in pretrained model\n",
      "2022-02-22 16:27:12 [WARNING]\tneck.yolo_block.2.conv_module.conv3.batch_norm.bias is not in pretrained model\n",
      "2022-02-22 16:27:12 [WARNING]\tneck.yolo_block.2.conv_module.conv3.batch_norm._mean is not in pretrained model\n",
      "2022-02-22 16:27:12 [WARNING]\tneck.yolo_block.2.conv_module.conv3.batch_norm._variance is not in pretrained model\n",
      "2022-02-22 16:27:12 [WARNING]\tneck.yolo_block.2.conv_module.route.conv.weight is not in pretrained model\n",
      "2022-02-22 16:27:12 [WARNING]\tneck.yolo_block.2.conv_module.route.batch_norm.weight is not in pretrained model\n",
      "2022-02-22 16:27:12 [WARNING]\tneck.yolo_block.2.conv_module.route.batch_norm.bias is not in pretrained model\n",
      "2022-02-22 16:27:12 [WARNING]\tneck.yolo_block.2.conv_module.route.batch_norm._mean is not in pretrained model\n",
      "2022-02-22 16:27:12 [WARNING]\tneck.yolo_block.2.conv_module.route.batch_norm._variance is not in pretrained model\n",
      "2022-02-22 16:27:12 [WARNING]\tneck.yolo_block.2.tip.conv.weight is not in pretrained model\n",
      "2022-02-22 16:27:12 [WARNING]\tneck.yolo_block.2.tip.batch_norm.weight is not in pretrained model\n",
      "2022-02-22 16:27:12 [WARNING]\tneck.yolo_block.2.tip.batch_norm.bias is not in pretrained model\n",
      "2022-02-22 16:27:12 [WARNING]\tneck.yolo_block.2.tip.batch_norm._mean is not in pretrained model\n",
      "2022-02-22 16:27:12 [WARNING]\tneck.yolo_block.2.tip.batch_norm._variance is not in pretrained model\n",
      "2022-02-22 16:27:12 [WARNING]\tyolo_head.yolo_output.0.weight is not in pretrained model\n",
      "2022-02-22 16:27:12 [WARNING]\tyolo_head.yolo_output.0.bias is not in pretrained model\n",
      "2022-02-22 16:27:12 [WARNING]\tyolo_head.yolo_output.1.weight is not in pretrained model\n",
      "2022-02-22 16:27:12 [WARNING]\tyolo_head.yolo_output.1.bias is not in pretrained model\n",
      "2022-02-22 16:27:12 [WARNING]\tyolo_head.yolo_output.2.weight is not in pretrained model\n",
      "2022-02-22 16:27:12 [WARNING]\tyolo_head.yolo_output.2.bias is not in pretrained model\n",
      "2022-02-22 16:27:13 [INFO]\tThere are 260/366 variables loaded into YOLOv3.\n",
      "2022-02-22 16:27:22 [INFO]\t[TRAIN] Epoch=1/30, Step=10/175, loss_xy=18.231983, loss_wh=23.833302, loss_obj=8210.858398, loss_cls=15.306124, loss=8268.229492, lr=0.000001, time_each_step=0.87s, eta=1:16:51\n",
      "2022-02-22 16:27:29 [INFO]\t[TRAIN] Epoch=1/30, Step=20/175, loss_xy=19.401205, loss_wh=22.469288, loss_obj=220.350891, loss_cls=12.870644, loss=275.092010, lr=0.000002, time_each_step=0.77s, eta=1:7:40\n",
      "2022-02-22 16:27:37 [INFO]\t[TRAIN] Epoch=1/30, Step=30/175, loss_xy=17.868345, loss_wh=19.152781, loss_obj=80.999069, loss_cls=12.921139, loss=130.941330, lr=0.000004, time_each_step=0.71s, eta=1:2:16\n",
      "2022-02-22 16:27:45 [INFO]\t[TRAIN] Epoch=1/30, Step=40/175, loss_xy=22.295740, loss_wh=23.056593, loss_obj=69.235329, loss_cls=16.973623, loss=131.561279, lr=0.000005, time_each_step=0.84s, eta=1:13:23\n",
      "2022-02-22 16:27:51 [INFO]\t[TRAIN] Epoch=1/30, Step=50/175, loss_xy=16.361519, loss_wh=17.210884, loss_obj=54.070675, loss_cls=11.275349, loss=98.918427, lr=0.000006, time_each_step=0.64s, eta=0:56:20\n",
      "2022-02-22 16:28:00 [INFO]\t[TRAIN] Epoch=1/30, Step=60/175, loss_xy=14.304664, loss_wh=13.483542, loss_obj=45.571880, loss_cls=9.957299, loss=83.317383, lr=0.000007, time_each_step=0.87s, eta=1:15:57\n",
      "2022-02-22 16:28:06 [INFO]\t[TRAIN] Epoch=1/30, Step=70/175, loss_xy=12.932606, loss_wh=14.492397, loss_obj=43.427608, loss_cls=10.082522, loss=80.935135, lr=0.000009, time_each_step=0.63s, eta=0:54:53\n",
      "2022-02-22 16:28:15 [INFO]\t[TRAIN] Epoch=1/30, Step=80/175, loss_xy=13.018344, loss_wh=12.791455, loss_obj=38.064751, loss_cls=8.949823, loss=72.824371, lr=0.000010, time_each_step=0.82s, eta=1:10:54\n",
      "2022-02-22 16:28:22 [INFO]\t[TRAIN] Epoch=1/30, Step=90/175, loss_xy=18.478054, loss_wh=17.746176, loss_obj=54.609310, loss_cls=12.769881, loss=103.603424, lr=0.000011, time_each_step=0.79s, eta=1:8:50\n",
      "2022-02-22 16:28:30 [INFO]\t[TRAIN] Epoch=1/30, Step=100/175, loss_xy=17.221617, loss_wh=14.786865, loss_obj=50.495979, loss_cls=12.174536, loss=94.679001, lr=0.000012, time_each_step=0.71s, eta=1:1:48\n",
      "2022-02-22 16:28:37 [INFO]\t[TRAIN] Epoch=1/30, Step=110/175, loss_xy=18.850368, loss_wh=15.610472, loss_obj=51.397331, loss_cls=12.811535, loss=98.669708, lr=0.000014, time_each_step=0.71s, eta=1:1:19\n",
      "2022-02-22 16:28:44 [INFO]\t[TRAIN] Epoch=1/30, Step=120/175, loss_xy=12.003748, loss_wh=10.568148, loss_obj=29.737766, loss_cls=7.524649, loss=59.834312, lr=0.000015, time_each_step=0.73s, eta=1:2:56\n",
      "2022-02-22 16:28:51 [INFO]\t[TRAIN] Epoch=1/30, Step=130/175, loss_xy=14.970215, loss_wh=11.971652, loss_obj=39.627151, loss_cls=9.363153, loss=75.932167, lr=0.000016, time_each_step=0.68s, eta=0:58:54\n",
      "2022-02-22 16:28:58 [INFO]\t[TRAIN] Epoch=1/30, Step=140/175, loss_xy=13.478437, loss_wh=11.911747, loss_obj=40.246773, loss_cls=8.620793, loss=74.257751, lr=0.000017, time_each_step=0.74s, eta=1:3:46\n",
      "2022-02-22 16:29:05 [INFO]\t[TRAIN] Epoch=1/30, Step=150/175, loss_xy=15.154284, loss_wh=12.121482, loss_obj=35.092045, loss_cls=8.456875, loss=70.824692, lr=0.000019, time_each_step=0.68s, eta=0:58:43\n",
      "2022-02-22 16:29:12 [INFO]\t[TRAIN] Epoch=1/30, Step=160/175, loss_xy=12.633397, loss_wh=10.265819, loss_obj=32.499912, loss_cls=7.912848, loss=63.311977, lr=0.000020, time_each_step=0.69s, eta=0:59:0\n",
      "2022-02-22 16:29:19 [INFO]\t[TRAIN] Epoch=1/30, Step=170/175, loss_xy=15.120138, loss_wh=12.378530, loss_obj=33.339912, loss_cls=8.863617, loss=69.702194, lr=0.000021, time_each_step=0.68s, eta=0:58:13\n",
      "2022-02-22 16:29:23 [INFO]\t[TRAIN] Epoch 1 finished, loss_xy=16.33685, loss_wh=15.602425, loss_obj=751.595, loss_cls=11.189982, loss=794.7243 .\n",
      "2022-02-22 16:29:28 [INFO]\t[TRAIN] Epoch=2/30, Step=5/175, loss_xy=13.212104, loss_wh=10.417521, loss_obj=34.605064, loss_cls=7.595101, loss=65.829788, lr=0.000022, time_each_step=0.86s, eta=1:13:7\n",
      "2022-02-22 16:29:36 [INFO]\t[TRAIN] Epoch=2/30, Step=15/175, loss_xy=10.275391, loss_wh=8.830798, loss_obj=26.181561, loss_cls=6.006691, loss=51.294441, lr=0.000024, time_each_step=0.85s, eta=1:12:21\n",
      "2022-02-22 16:29:44 [INFO]\t[TRAIN] Epoch=2/30, Step=25/175, loss_xy=15.468725, loss_wh=11.221399, loss_obj=32.259621, loss_cls=9.635029, loss=68.584778, lr=0.000025, time_each_step=0.8s, eta=1:8:11\n",
      "2022-02-22 16:29:52 [INFO]\t[TRAIN] Epoch=2/30, Step=35/175, loss_xy=13.799367, loss_wh=12.311027, loss_obj=29.896469, loss_cls=7.963360, loss=63.970222, lr=0.000026, time_each_step=0.75s, eta=1:3:41\n",
      "2022-02-22 16:29:59 [INFO]\t[TRAIN] Epoch=2/30, Step=45/175, loss_xy=14.531214, loss_wh=10.801395, loss_obj=37.869019, loss_cls=7.334265, loss=70.535896, lr=0.000027, time_each_step=0.77s, eta=1:4:57\n",
      "2022-02-22 16:30:07 [INFO]\t[TRAIN] Epoch=2/30, Step=55/175, loss_xy=16.029167, loss_wh=11.400720, loss_obj=36.481983, loss_cls=9.122536, loss=73.034409, lr=0.000029, time_each_step=0.75s, eta=1:3:34\n",
      "2022-02-22 16:30:12 [INFO]\t[TRAIN] Epoch=2/30, Step=65/175, loss_xy=15.185850, loss_wh=10.225544, loss_obj=30.857500, loss_cls=8.615414, loss=64.884308, lr=0.000030, time_each_step=0.56s, eta=0:47:37\n",
      "2022-02-22 16:30:20 [INFO]\t[TRAIN] Epoch=2/30, Step=75/175, loss_xy=12.373077, loss_wh=9.035598, loss_obj=26.999067, loss_cls=6.379000, loss=54.786743, lr=0.000031, time_each_step=0.75s, eta=1:2:56\n",
      "2022-02-22 16:30:27 [INFO]\t[TRAIN] Epoch=2/30, Step=85/175, loss_xy=16.172380, loss_wh=11.567509, loss_obj=30.756817, loss_cls=8.112735, loss=66.609436, lr=0.000032, time_each_step=0.74s, eta=1:2:1\n",
      "2022-02-22 16:30:35 [INFO]\t[TRAIN] Epoch=2/30, Step=95/175, loss_xy=15.690318, loss_wh=10.701370, loss_obj=34.304329, loss_cls=7.682534, loss=68.378555, lr=0.000034, time_each_step=0.73s, eta=1:1:19\n",
      "2022-02-22 16:30:42 [INFO]\t[TRAIN] Epoch=2/30, Step=105/175, loss_xy=13.439559, loss_wh=8.973396, loss_obj=31.848284, loss_cls=8.102407, loss=62.363647, lr=0.000035, time_each_step=0.79s, eta=1:5:52\n",
      "2022-02-22 16:30:49 [INFO]\t[TRAIN] Epoch=2/30, Step=115/175, loss_xy=10.627207, loss_wh=8.334038, loss_obj=25.011812, loss_cls=5.081876, loss=49.054932, lr=0.000036, time_each_step=0.66s, eta=0:54:54\n",
      "2022-02-22 16:30:56 [INFO]\t[TRAIN] Epoch=2/30, Step=125/175, loss_xy=10.025078, loss_wh=5.916333, loss_obj=24.253395, loss_cls=4.649738, loss=44.844543, lr=0.000037, time_each_step=0.67s, eta=0:56:5\n",
      "2022-02-22 16:31:03 [INFO]\t[TRAIN] Epoch=2/30, Step=135/175, loss_xy=14.713013, loss_wh=8.348596, loss_obj=29.728977, loss_cls=5.867607, loss=58.658192, lr=0.000039, time_each_step=0.74s, eta=1:1:17\n",
      "2022-02-22 16:31:11 [INFO]\t[TRAIN] Epoch=2/30, Step=145/175, loss_xy=16.845402, loss_wh=10.033453, loss_obj=38.745773, loss_cls=10.152393, loss=75.777023, lr=0.000040, time_each_step=0.8s, eta=1:6:34\n",
      "2022-02-22 16:31:19 [INFO]\t[TRAIN] Epoch=2/30, Step=155/175, loss_xy=12.964018, loss_wh=7.407773, loss_obj=26.089005, loss_cls=4.488742, loss=50.949539, lr=0.000041, time_each_step=0.76s, eta=1:2:44\n",
      "2022-02-22 16:31:26 [INFO]\t[TRAIN] Epoch=2/30, Step=165/175, loss_xy=13.675287, loss_wh=8.366280, loss_obj=29.796947, loss_cls=7.436320, loss=59.274837, lr=0.000042, time_each_step=0.74s, eta=1:1:19\n",
      "2022-02-22 16:31:33 [INFO]\t[TRAIN] Epoch=2/30, Step=175/175, loss_xy=12.209246, loss_wh=7.288860, loss_obj=22.155087, loss_cls=5.091161, loss=46.744350, lr=0.000044, time_each_step=0.72s, eta=0:59:2\n",
      "2022-02-22 16:31:33 [INFO]\t[TRAIN] Epoch 2 finished, loss_xy=14.346577, loss_wh=9.854735, loss_obj=30.686625, loss_cls=7.618057, loss=62.505993 .\n",
      "2022-02-22 16:31:42 [INFO]\t[TRAIN] Epoch=3/30, Step=10/175, loss_xy=13.292250, loss_wh=7.226892, loss_obj=26.237041, loss_cls=5.684000, loss=52.440186, lr=0.000045, time_each_step=0.82s, eta=1:7:32\n",
      "2022-02-22 16:31:51 [INFO]\t[TRAIN] Epoch=3/30, Step=20/175, loss_xy=14.537925, loss_wh=8.575319, loss_obj=29.134605, loss_cls=7.870142, loss=60.117992, lr=0.000046, time_each_step=0.89s, eta=1:12:55\n",
      "2022-02-22 16:31:59 [INFO]\t[TRAIN] Epoch=3/30, Step=30/175, loss_xy=15.563503, loss_wh=9.428963, loss_obj=32.314793, loss_cls=7.957530, loss=65.264786, lr=0.000047, time_each_step=0.86s, eta=1:10:11\n",
      "2022-02-22 16:32:06 [INFO]\t[TRAIN] Epoch=3/30, Step=40/175, loss_xy=13.097702, loss_wh=6.729583, loss_obj=21.765133, loss_cls=5.649835, loss=47.242252, lr=0.000049, time_each_step=0.68s, eta=0:55:37\n",
      "2022-02-22 16:32:13 [INFO]\t[TRAIN] Epoch=3/30, Step=50/175, loss_xy=12.998569, loss_wh=6.938806, loss_obj=24.818476, loss_cls=7.338007, loss=52.093857, lr=0.000050, time_each_step=0.74s, eta=1:0:32\n",
      "2022-02-22 16:32:22 [INFO]\t[TRAIN] Epoch=3/30, Step=60/175, loss_xy=13.823690, loss_wh=7.574979, loss_obj=31.613047, loss_cls=6.051969, loss=59.063686, lr=0.000051, time_each_step=0.85s, eta=1:9:2\n",
      "2022-02-22 16:32:28 [INFO]\t[TRAIN] Epoch=3/30, Step=70/175, loss_xy=13.789040, loss_wh=7.121902, loss_obj=24.857948, loss_cls=5.983208, loss=51.752098, lr=0.000052, time_each_step=0.63s, eta=0:50:59\n",
      "2022-02-22 16:32:35 [INFO]\t[TRAIN] Epoch=3/30, Step=80/175, loss_xy=12.435980, loss_wh=6.604447, loss_obj=23.975540, loss_cls=5.802673, loss=48.818642, lr=0.000054, time_each_step=0.68s, eta=0:54:58\n",
      "2022-02-22 16:32:43 [INFO]\t[TRAIN] Epoch=3/30, Step=90/175, loss_xy=15.215510, loss_wh=7.461806, loss_obj=27.539879, loss_cls=6.925004, loss=57.142197, lr=0.000055, time_each_step=0.86s, eta=1:9:20\n",
      "2022-02-22 16:32:50 [INFO]\t[TRAIN] Epoch=3/30, Step=100/175, loss_xy=13.702190, loss_wh=7.396504, loss_obj=20.862947, loss_cls=4.902655, loss=46.864296, lr=0.000056, time_each_step=0.68s, eta=0:55:6\n",
      "2022-02-22 16:32:57 [INFO]\t[TRAIN] Epoch=3/30, Step=110/175, loss_xy=13.302046, loss_wh=6.833586, loss_obj=24.157946, loss_cls=6.082007, loss=50.375587, lr=0.000057, time_each_step=0.63s, eta=0:51:0\n",
      "2022-02-22 16:33:04 [INFO]\t[TRAIN] Epoch=3/30, Step=120/175, loss_xy=10.640022, loss_wh=5.928042, loss_obj=24.404493, loss_cls=4.329288, loss=45.301846, lr=0.000059, time_each_step=0.79s, eta=1:3:30\n",
      "2022-02-22 16:33:12 [INFO]\t[TRAIN] Epoch=3/30, Step=130/175, loss_xy=18.061329, loss_wh=8.737835, loss_obj=32.265629, loss_cls=9.611292, loss=68.676086, lr=0.000060, time_each_step=0.77s, eta=1:2:12\n",
      "2022-02-22 16:33:20 [INFO]\t[TRAIN] Epoch=3/30, Step=140/175, loss_xy=12.354372, loss_wh=7.011963, loss_obj=23.438345, loss_cls=4.760503, loss=47.565182, lr=0.000061, time_each_step=0.78s, eta=1:2:11\n",
      "2022-02-22 16:33:29 [INFO]\t[TRAIN] Epoch=3/30, Step=150/175, loss_xy=11.847555, loss_wh=5.454584, loss_obj=21.319250, loss_cls=4.731281, loss=43.352673, lr=0.000062, time_each_step=0.86s, eta=1:9:0\n",
      "2022-02-22 16:33:36 [INFO]\t[TRAIN] Epoch=3/30, Step=160/175, loss_xy=13.187201, loss_wh=6.581559, loss_obj=25.613846, loss_cls=5.799296, loss=51.181904, lr=0.000064, time_each_step=0.73s, eta=0:58:24\n",
      "2022-02-22 16:33:44 [INFO]\t[TRAIN] Epoch=3/30, Step=170/175, loss_xy=9.913829, loss_wh=5.001224, loss_obj=20.866810, loss_cls=4.165336, loss=39.947197, lr=0.000065, time_each_step=0.81s, eta=1:4:18\n",
      "2022-02-22 16:33:48 [INFO]\t[TRAIN] Epoch 3 finished, loss_xy=13.499238, loss_wh=7.082797, loss_obj=25.389746, loss_cls=6.150973, loss=52.122757 .\n",
      "2022-02-22 16:33:54 [INFO]\t[TRAIN] Epoch=4/30, Step=5/175, loss_xy=13.187650, loss_wh=5.509307, loss_obj=20.410534, loss_cls=7.206472, loss=46.313965, lr=0.000066, time_each_step=0.94s, eta=1:14:55\n",
      "2022-02-22 16:34:00 [INFO]\t[TRAIN] Epoch=4/30, Step=15/175, loss_xy=10.400925, loss_wh=5.870296, loss_obj=19.804642, loss_cls=5.598763, loss=41.674625, lr=0.000067, time_each_step=0.68s, eta=0:53:48\n",
      "2022-02-22 16:34:08 [INFO]\t[TRAIN] Epoch=4/30, Step=25/175, loss_xy=11.772318, loss_wh=5.707395, loss_obj=20.757397, loss_cls=4.844297, loss=43.081406, lr=0.000069, time_each_step=0.79s, eta=1:2:26\n",
      "2022-02-22 16:34:16 [INFO]\t[TRAIN] Epoch=4/30, Step=35/175, loss_xy=14.772449, loss_wh=6.480693, loss_obj=25.481318, loss_cls=6.658546, loss=53.393005, lr=0.000070, time_each_step=0.81s, eta=1:3:55\n",
      "2022-02-22 16:34:25 [INFO]\t[TRAIN] Epoch=4/30, Step=45/175, loss_xy=14.120454, loss_wh=6.564303, loss_obj=28.620209, loss_cls=5.062479, loss=54.367447, lr=0.000071, time_each_step=0.86s, eta=1:7:57\n",
      "2022-02-22 16:34:32 [INFO]\t[TRAIN] Epoch=4/30, Step=55/175, loss_xy=16.312305, loss_wh=8.426624, loss_obj=35.911293, loss_cls=8.063520, loss=68.713745, lr=0.000072, time_each_step=0.74s, eta=0:58:28\n",
      "2022-02-22 16:34:41 [INFO]\t[TRAIN] Epoch=4/30, Step=65/175, loss_xy=11.287737, loss_wh=5.268801, loss_obj=21.102617, loss_cls=6.698661, loss=44.357819, lr=0.000074, time_each_step=0.82s, eta=1:4:5\n",
      "2022-02-22 16:34:48 [INFO]\t[TRAIN] Epoch=4/30, Step=75/175, loss_xy=14.699806, loss_wh=5.928881, loss_obj=24.737558, loss_cls=6.704580, loss=52.070824, lr=0.000075, time_each_step=0.74s, eta=0:58:16\n",
      "2022-02-22 16:34:56 [INFO]\t[TRAIN] Epoch=4/30, Step=85/175, loss_xy=13.366325, loss_wh=5.565415, loss_obj=24.398420, loss_cls=6.348176, loss=49.678337, lr=0.000076, time_each_step=0.83s, eta=1:4:59\n",
      "2022-02-22 16:35:03 [INFO]\t[TRAIN] Epoch=4/30, Step=95/175, loss_xy=12.361341, loss_wh=4.876985, loss_obj=19.141153, loss_cls=4.551808, loss=40.931286, lr=0.000077, time_each_step=0.66s, eta=0:51:22\n",
      "2022-02-22 16:35:11 [INFO]\t[TRAIN] Epoch=4/30, Step=105/175, loss_xy=13.877064, loss_wh=5.959445, loss_obj=24.609879, loss_cls=4.809835, loss=49.256222, lr=0.000079, time_each_step=0.86s, eta=1:6:41\n",
      "2022-02-22 16:35:18 [INFO]\t[TRAIN] Epoch=4/30, Step=115/175, loss_xy=10.301805, loss_wh=4.256481, loss_obj=17.233829, loss_cls=3.842317, loss=35.634430, lr=0.000080, time_each_step=0.64s, eta=0:49:19\n",
      "2022-02-22 16:35:25 [INFO]\t[TRAIN] Epoch=4/30, Step=125/175, loss_xy=14.046951, loss_wh=5.673292, loss_obj=24.163380, loss_cls=7.356021, loss=51.239643, lr=0.000081, time_each_step=0.72s, eta=0:55:49\n",
      "2022-02-22 16:35:32 [INFO]\t[TRAIN] Epoch=4/30, Step=135/175, loss_xy=15.644110, loss_wh=6.133079, loss_obj=27.593582, loss_cls=9.481091, loss=58.851860, lr=0.000082, time_each_step=0.74s, eta=0:56:53\n",
      "2022-02-22 16:35:39 [INFO]\t[TRAIN] Epoch=4/30, Step=145/175, loss_xy=16.613821, loss_wh=5.764426, loss_obj=24.733917, loss_cls=9.504942, loss=56.617104, lr=0.000084, time_each_step=0.7s, eta=0:53:56\n",
      "2022-02-22 16:35:46 [INFO]\t[TRAIN] Epoch=4/30, Step=155/175, loss_xy=9.749586, loss_wh=3.776041, loss_obj=17.810495, loss_cls=4.051159, loss=35.387283, lr=0.000085, time_each_step=0.71s, eta=0:54:25\n",
      "2022-02-22 16:35:53 [INFO]\t[TRAIN] Epoch=4/30, Step=165/175, loss_xy=11.839425, loss_wh=4.294971, loss_obj=20.165709, loss_cls=5.446835, loss=41.746937, lr=0.000086, time_each_step=0.7s, eta=0:53:33\n",
      "2022-02-22 16:36:01 [INFO]\t[TRAIN] Epoch=4/30, Step=175/175, loss_xy=13.161558, loss_wh=4.768238, loss_obj=21.685562, loss_cls=7.025412, loss=46.640770, lr=0.000087, time_each_step=0.74s, eta=0:56:40\n",
      "2022-02-22 16:36:01 [INFO]\t[TRAIN] Epoch 4 finished, loss_xy=12.976582, loss_wh=5.5638433, loss_obj=22.805048, loss_cls=5.6796103, loss=47.025078 .\n",
      "2022-02-22 16:36:32 [INFO]\t[TRAIN] Epoch=5/30, Step=40/175, loss_xy=11.437077, loss_wh=4.807506, loss_obj=21.331957, loss_cls=3.563086, loss=41.139626, lr=0.000092, time_each_step=0.78s, eta=0:59:25\n",
      "2022-02-22 16:36:39 [INFO]\t[TRAIN] Epoch=5/30, Step=50/175, loss_xy=13.202891, loss_wh=5.180548, loss_obj=20.138554, loss_cls=5.244825, loss=43.766823, lr=0.000094, time_each_step=0.72s, eta=0:54:31\n",
      "2022-02-22 16:36:48 [INFO]\t[TRAIN] Epoch=5/30, Step=60/175, loss_xy=11.024678, loss_wh=5.372215, loss_obj=17.471790, loss_cls=4.140345, loss=38.009026, lr=0.000095, time_each_step=0.83s, eta=1:2:46\n",
      "2022-02-22 16:36:55 [INFO]\t[TRAIN] Epoch=5/30, Step=70/175, loss_xy=14.458191, loss_wh=5.675221, loss_obj=21.491905, loss_cls=8.058147, loss=49.683464, lr=0.000096, time_each_step=0.69s, eta=0:52:20\n",
      "2022-02-22 16:37:01 [INFO]\t[TRAIN] Epoch=5/30, Step=80/175, loss_xy=12.918728, loss_wh=4.743268, loss_obj=20.703905, loss_cls=5.985056, loss=44.350952, lr=0.000097, time_each_step=0.67s, eta=0:50:6\n",
      "2022-02-22 16:37:08 [INFO]\t[TRAIN] Epoch=5/30, Step=90/175, loss_xy=14.398983, loss_wh=4.905715, loss_obj=20.786379, loss_cls=7.664240, loss=47.755318, lr=0.000099, time_each_step=0.71s, eta=0:53:23\n",
      "2022-02-22 16:37:17 [INFO]\t[TRAIN] Epoch=5/30, Step=100/175, loss_xy=13.542990, loss_wh=6.878109, loss_obj=22.999496, loss_cls=6.528608, loss=49.949203, lr=0.000100, time_each_step=0.9s, eta=1:7:26\n",
      "2022-02-22 16:37:25 [INFO]\t[TRAIN] Epoch=5/30, Step=110/175, loss_xy=11.437715, loss_wh=4.017673, loss_obj=17.632830, loss_cls=4.553420, loss=37.641640, lr=0.000101, time_each_step=0.72s, eta=0:53:52\n",
      "2022-02-22 16:37:33 [INFO]\t[TRAIN] Epoch=5/30, Step=120/175, loss_xy=12.485476, loss_wh=4.468793, loss_obj=17.670591, loss_cls=4.382527, loss=39.007389, lr=0.000102, time_each_step=0.87s, eta=1:4:47\n",
      "2022-02-22 16:37:42 [INFO]\t[TRAIN] Epoch=5/30, Step=130/175, loss_xy=12.826149, loss_wh=4.671642, loss_obj=20.977634, loss_cls=3.662166, loss=42.137592, lr=0.000104, time_each_step=0.84s, eta=1:2:17\n",
      "2022-02-22 16:37:48 [INFO]\t[TRAIN] Epoch=5/30, Step=140/175, loss_xy=15.606425, loss_wh=5.202719, loss_obj=22.263409, loss_cls=6.372845, loss=49.445396, lr=0.000105, time_each_step=0.66s, eta=0:48:53\n",
      "2022-02-22 16:37:56 [INFO]\t[TRAIN] Epoch=5/30, Step=150/175, loss_xy=9.685871, loss_wh=3.873945, loss_obj=13.741554, loss_cls=3.082325, loss=30.383694, lr=0.000106, time_each_step=0.74s, eta=0:54:37\n",
      "2022-02-22 16:38:03 [INFO]\t[TRAIN] Epoch=5/30, Step=160/175, loss_xy=13.131557, loss_wh=4.371529, loss_obj=20.467876, loss_cls=5.856184, loss=43.827145, lr=0.000107, time_each_step=0.77s, eta=0:57:17\n",
      "2022-02-22 16:38:10 [INFO]\t[TRAIN] Epoch=5/30, Step=170/175, loss_xy=10.678333, loss_wh=4.031731, loss_obj=17.375034, loss_cls=3.937217, loss=36.022316, lr=0.000109, time_each_step=0.67s, eta=0:49:30\n",
      "2022-02-22 16:38:14 [INFO]\t[TRAIN] Epoch 5 finished, loss_xy=12.406148, loss_wh=4.787449, loss_obj=20.34928, loss_cls=4.899268, loss=42.44215 .\n",
      "2022-02-22 16:38:14 [WARNING]\tDetector only supports single card evaluation with batch_size=1 during evaluation, so batch_size is forcibly set to 1.\n",
      "2022-02-22 16:38:14 [INFO]\tStart to evaluate(total_samples=1000, total_steps=1000)...\n",
      "2022-02-22 16:38:42 [INFO]\tAccumulating evaluatation results...\n",
      "2022-02-22 16:38:42 [INFO]\t[EVAL] Finished, Epoch=5, bbox_map=35.624517 .\n",
      "2022-02-22 16:38:44 [INFO]\tModel saved in output/yolov3_darknet53/best_model.\n",
      "2022-02-22 16:38:44 [INFO]\tCurrent evaluated best model on eval_dataset is epoch_5, bbox_map=35.62451696207669\n",
      "2022-02-22 16:38:45 [INFO]\tModel saved in output/yolov3_darknet53/epoch_5.\n",
      "2022-02-22 16:38:51 [INFO]\t[TRAIN] Epoch=6/30, Step=5/175, loss_xy=11.593330, loss_wh=3.959194, loss_obj=20.410259, loss_cls=5.976871, loss=41.939655, lr=0.000110, time_each_step=1.0s, eta=1:14:58\n",
      "2022-02-22 16:38:58 [INFO]\t[TRAIN] Epoch=6/30, Step=15/175, loss_xy=12.250495, loss_wh=4.601772, loss_obj=20.256920, loss_cls=4.937605, loss=42.046791, lr=0.000111, time_each_step=0.63s, eta=0:48:20\n",
      "2022-02-22 16:39:05 [INFO]\t[TRAIN] Epoch=6/30, Step=25/175, loss_xy=12.520101, loss_wh=4.797315, loss_obj=22.044811, loss_cls=6.340723, loss=45.702950, lr=0.000112, time_each_step=0.75s, eta=0:56:39\n",
      "2022-02-22 16:39:13 [INFO]\t[TRAIN] Epoch=6/30, Step=35/175, loss_xy=11.125876, loss_wh=3.706853, loss_obj=18.188450, loss_cls=3.793734, loss=36.814911, lr=0.000114, time_each_step=0.74s, eta=0:55:44\n",
      "2022-02-22 16:39:20 [INFO]\t[TRAIN] Epoch=6/30, Step=45/175, loss_xy=13.336113, loss_wh=4.764443, loss_obj=20.835747, loss_cls=4.448859, loss=43.385162, lr=0.000115, time_each_step=0.76s, eta=0:57:6\n",
      "2022-02-22 16:39:28 [INFO]\t[TRAIN] Epoch=6/30, Step=55/175, loss_xy=9.464060, loss_wh=3.176311, loss_obj=16.139065, loss_cls=2.973876, loss=31.753311, lr=0.000116, time_each_step=0.78s, eta=0:58:42\n",
      "2022-02-22 16:39:36 [INFO]\t[TRAIN] Epoch=6/30, Step=65/175, loss_xy=11.799877, loss_wh=3.699877, loss_obj=21.851936, loss_cls=5.131592, loss=42.483284, lr=0.000117, time_each_step=0.78s, eta=0:58:14\n",
      "2022-02-22 16:39:43 [INFO]\t[TRAIN] Epoch=6/30, Step=75/175, loss_xy=11.573273, loss_wh=3.677479, loss_obj=19.621649, loss_cls=4.519241, loss=39.391640, lr=0.000119, time_each_step=0.73s, eta=0:55:6\n",
      "2022-02-22 16:39:50 [INFO]\t[TRAIN] Epoch=6/30, Step=85/175, loss_xy=14.915186, loss_wh=5.312469, loss_obj=23.611021, loss_cls=4.968472, loss=48.807148, lr=0.000120, time_each_step=0.69s, eta=0:51:39\n",
      "2022-02-22 16:39:57 [INFO]\t[TRAIN] Epoch=6/30, Step=95/175, loss_xy=12.652976, loss_wh=4.829411, loss_obj=19.236298, loss_cls=3.971872, loss=40.690556, lr=0.000121, time_each_step=0.71s, eta=0:52:56\n",
      "2022-02-22 16:40:05 [INFO]\t[TRAIN] Epoch=6/30, Step=105/175, loss_xy=9.603905, loss_wh=3.475383, loss_obj=16.785479, loss_cls=2.968174, loss=32.832939, lr=0.000122, time_each_step=0.76s, eta=0:56:44\n",
      "2022-02-22 16:40:12 [INFO]\t[TRAIN] Epoch=6/30, Step=115/175, loss_xy=11.344310, loss_wh=5.012894, loss_obj=15.607218, loss_cls=3.550133, loss=35.514553, lr=0.000124, time_each_step=0.76s, eta=0:56:17\n",
      "2022-02-22 16:40:21 [INFO]\t[TRAIN] Epoch=6/30, Step=125/175, loss_xy=11.501189, loss_wh=3.534397, loss_obj=16.964329, loss_cls=3.909241, loss=35.909157, lr=0.000125, time_each_step=0.87s, eta=1:4:27\n",
      "2022-02-22 16:40:29 [INFO]\t[TRAIN] Epoch=6/30, Step=135/175, loss_xy=12.350564, loss_wh=5.441216, loss_obj=19.687960, loss_cls=5.581107, loss=43.060844, lr=0.000125, time_each_step=0.79s, eta=0:58:27\n",
      "2022-02-22 16:40:36 [INFO]\t[TRAIN] Epoch=6/30, Step=145/175, loss_xy=12.777980, loss_wh=4.682183, loss_obj=16.570328, loss_cls=4.116060, loss=38.146553, lr=0.000125, time_each_step=0.69s, eta=0:51:5\n",
      "2022-02-22 16:40:44 [INFO]\t[TRAIN] Epoch=6/30, Step=155/175, loss_xy=11.014809, loss_wh=3.833910, loss_obj=16.755299, loss_cls=4.154016, loss=35.758034, lr=0.000125, time_each_step=0.78s, eta=0:57:15\n",
      "2022-02-22 16:40:50 [INFO]\t[TRAIN] Epoch=6/30, Step=165/175, loss_xy=12.817145, loss_wh=4.150496, loss_obj=23.147612, loss_cls=4.540841, loss=44.656094, lr=0.000125, time_each_step=0.68s, eta=0:50:15\n",
      "2022-02-22 16:40:58 [INFO]\t[TRAIN] Epoch=6/30, Step=175/175, loss_xy=13.462646, loss_wh=4.164392, loss_obj=19.695816, loss_cls=3.996688, loss=41.319542, lr=0.000125, time_each_step=0.73s, eta=0:53:45\n",
      "2022-02-22 16:40:58 [INFO]\t[TRAIN] Epoch 6 finished, loss_xy=12.287428, loss_wh=4.2723374, loss_obj=19.642145, loss_cls=4.364294, loss=40.56621 .\n",
      "2022-02-22 16:41:06 [INFO]\t[TRAIN] Epoch=7/30, Step=10/175, loss_xy=9.525470, loss_wh=3.484146, loss_obj=16.567337, loss_cls=3.520949, loss=33.097900, lr=0.000125, time_each_step=0.83s, eta=1:0:13\n",
      "2022-02-22 16:41:14 [INFO]\t[TRAIN] Epoch=7/30, Step=20/175, loss_xy=15.801435, loss_wh=4.906977, loss_obj=25.350193, loss_cls=3.899482, loss=49.958088, lr=0.000125, time_each_step=0.79s, eta=0:57:39\n",
      "2022-02-22 16:41:21 [INFO]\t[TRAIN] Epoch=7/30, Step=30/175, loss_xy=11.777333, loss_wh=4.310506, loss_obj=18.202160, loss_cls=2.987271, loss=37.277271, lr=0.000125, time_each_step=0.72s, eta=0:52:25\n",
      "2022-02-22 16:41:29 [INFO]\t[TRAIN] Epoch=7/30, Step=40/175, loss_xy=13.533849, loss_wh=4.303873, loss_obj=22.429470, loss_cls=3.415690, loss=43.682884, lr=0.000125, time_each_step=0.77s, eta=0:55:54\n",
      "2022-02-22 16:41:37 [INFO]\t[TRAIN] Epoch=7/30, Step=50/175, loss_xy=11.513876, loss_wh=4.165061, loss_obj=17.270718, loss_cls=2.934585, loss=35.884239, lr=0.000125, time_each_step=0.81s, eta=0:58:24\n",
      "2022-02-22 16:41:43 [INFO]\t[TRAIN] Epoch=7/30, Step=60/175, loss_xy=9.986834, loss_wh=3.381978, loss_obj=15.997305, loss_cls=1.754540, loss=31.120655, lr=0.000125, time_each_step=0.64s, eta=0:46:34\n",
      "2022-02-22 16:41:51 [INFO]\t[TRAIN] Epoch=7/30, Step=70/175, loss_xy=13.885863, loss_wh=4.265239, loss_obj=21.595802, loss_cls=5.547370, loss=45.294273, lr=0.000125, time_each_step=0.74s, eta=0:53:26\n",
      "2022-02-22 16:41:58 [INFO]\t[TRAIN] Epoch=7/30, Step=80/175, loss_xy=17.043030, loss_wh=5.306960, loss_obj=25.792881, loss_cls=5.650725, loss=53.793598, lr=0.000125, time_each_step=0.74s, eta=0:53:0\n",
      "2022-02-22 16:42:04 [INFO]\t[TRAIN] Epoch=7/30, Step=90/175, loss_xy=12.417187, loss_wh=4.407800, loss_obj=18.241655, loss_cls=4.226826, loss=39.293468, lr=0.000125, time_each_step=0.59s, eta=0:42:57\n",
      "2022-02-22 16:42:12 [INFO]\t[TRAIN] Epoch=7/30, Step=100/175, loss_xy=11.930537, loss_wh=3.492354, loss_obj=18.942984, loss_cls=3.858102, loss=38.223976, lr=0.000125, time_each_step=0.76s, eta=0:54:14\n",
      "2022-02-22 16:42:19 [INFO]\t[TRAIN] Epoch=7/30, Step=110/175, loss_xy=11.820396, loss_wh=4.324303, loss_obj=19.707283, loss_cls=3.679829, loss=39.531811, lr=0.000125, time_each_step=0.76s, eta=0:54:28\n",
      "2022-02-22 16:42:27 [INFO]\t[TRAIN] Epoch=7/30, Step=120/175, loss_xy=16.093138, loss_wh=5.681517, loss_obj=21.158552, loss_cls=5.707584, loss=48.640789, lr=0.000125, time_each_step=0.79s, eta=0:55:57\n",
      "2022-02-22 16:42:36 [INFO]\t[TRAIN] Epoch=7/30, Step=130/175, loss_xy=11.935253, loss_wh=4.114382, loss_obj=18.734966, loss_cls=3.154382, loss=37.938980, lr=0.000125, time_each_step=0.86s, eta=1:0:54\n",
      "2022-02-22 16:42:43 [INFO]\t[TRAIN] Epoch=7/30, Step=140/175, loss_xy=11.950991, loss_wh=3.851717, loss_obj=18.348492, loss_cls=3.043813, loss=37.195011, lr=0.000125, time_each_step=0.76s, eta=0:53:47\n",
      "2022-02-22 16:42:51 [INFO]\t[TRAIN] Epoch=7/30, Step=150/175, loss_xy=11.302878, loss_wh=3.617466, loss_obj=17.613338, loss_cls=2.534612, loss=35.068295, lr=0.000125, time_each_step=0.79s, eta=0:56:6\n",
      "2022-02-22 16:42:58 [INFO]\t[TRAIN] Epoch=7/30, Step=160/175, loss_xy=11.977703, loss_wh=3.343265, loss_obj=17.427103, loss_cls=4.857475, loss=37.605545, lr=0.000125, time_each_step=0.71s, eta=0:50:25\n",
      "2022-02-22 16:43:06 [INFO]\t[TRAIN] Epoch=7/30, Step=170/175, loss_xy=13.185204, loss_wh=4.449985, loss_obj=20.107759, loss_cls=5.505701, loss=43.248650, lr=0.000125, time_each_step=0.73s, eta=0:51:31\n",
      "2022-02-22 16:43:10 [INFO]\t[TRAIN] Epoch 7 finished, loss_xy=12.326039, loss_wh=4.101336, loss_obj=19.034319, loss_cls=3.6963463, loss=39.158043 .\n",
      "2022-02-22 16:43:16 [INFO]\t[TRAIN] Epoch=8/30, Step=5/175, loss_xy=12.596219, loss_wh=4.522686, loss_obj=21.041035, loss_cls=3.285855, loss=41.445793, lr=0.000125, time_each_step=0.98s, eta=1:8:20\n",
      "2022-02-22 16:43:23 [INFO]\t[TRAIN] Epoch=8/30, Step=15/175, loss_xy=12.127592, loss_wh=3.532723, loss_obj=17.486252, loss_cls=3.940526, loss=37.087093, lr=0.000125, time_each_step=0.76s, eta=0:53:32\n",
      "2022-02-22 16:43:31 [INFO]\t[TRAIN] Epoch=8/30, Step=25/175, loss_xy=10.665159, loss_wh=3.346184, loss_obj=15.753242, loss_cls=2.007738, loss=31.772324, lr=0.000125, time_each_step=0.75s, eta=0:52:38\n",
      "2022-02-22 16:43:38 [INFO]\t[TRAIN] Epoch=8/30, Step=35/175, loss_xy=15.003245, loss_wh=5.335746, loss_obj=20.877010, loss_cls=4.480993, loss=45.696999, lr=0.000125, time_each_step=0.72s, eta=0:50:40\n",
      "2022-02-22 16:43:47 [INFO]\t[TRAIN] Epoch=8/30, Step=45/175, loss_xy=11.897611, loss_wh=3.776434, loss_obj=15.365095, loss_cls=3.089447, loss=34.128586, lr=0.000125, time_each_step=0.92s, eta=1:3:32\n",
      "2022-02-22 16:43:54 [INFO]\t[TRAIN] Epoch=8/30, Step=55/175, loss_xy=11.942969, loss_wh=3.777279, loss_obj=19.269484, loss_cls=2.689590, loss=37.679321, lr=0.000125, time_each_step=0.7s, eta=0:48:36\n",
      "2022-02-22 16:44:01 [INFO]\t[TRAIN] Epoch=8/30, Step=65/175, loss_xy=10.358724, loss_wh=2.935985, loss_obj=16.981960, loss_cls=2.212008, loss=32.488678, lr=0.000125, time_each_step=0.7s, eta=0:48:50\n",
      "2022-02-22 16:44:08 [INFO]\t[TRAIN] Epoch=8/30, Step=75/175, loss_xy=12.456791, loss_wh=3.850661, loss_obj=19.912643, loss_cls=3.556725, loss=39.776817, lr=0.000125, time_each_step=0.69s, eta=0:47:59\n",
      "2022-02-22 16:44:16 [INFO]\t[TRAIN] Epoch=8/30, Step=85/175, loss_xy=12.948366, loss_wh=3.920467, loss_obj=19.416491, loss_cls=4.727257, loss=41.012581, lr=0.000125, time_each_step=0.72s, eta=0:50:1\n",
      "2022-02-22 16:44:23 [INFO]\t[TRAIN] Epoch=8/30, Step=95/175, loss_xy=12.457169, loss_wh=3.939250, loss_obj=23.673191, loss_cls=3.499071, loss=43.568680, lr=0.000125, time_each_step=0.72s, eta=0:49:41\n",
      "2022-02-22 16:44:30 [INFO]\t[TRAIN] Epoch=8/30, Step=105/175, loss_xy=10.985510, loss_wh=4.377172, loss_obj=17.740288, loss_cls=3.214174, loss=36.317142, lr=0.000125, time_each_step=0.72s, eta=0:49:38\n",
      "2022-02-22 16:44:37 [INFO]\t[TRAIN] Epoch=8/30, Step=115/175, loss_xy=11.249065, loss_wh=2.853031, loss_obj=17.302628, loss_cls=2.395885, loss=33.800610, lr=0.000125, time_each_step=0.75s, eta=0:51:5\n",
      "2022-02-22 16:44:45 [INFO]\t[TRAIN] Epoch=8/30, Step=125/175, loss_xy=16.781963, loss_wh=5.504873, loss_obj=21.903162, loss_cls=6.542628, loss=50.732628, lr=0.000125, time_each_step=0.72s, eta=0:49:19\n",
      "2022-02-22 16:44:52 [INFO]\t[TRAIN] Epoch=8/30, Step=135/175, loss_xy=11.814924, loss_wh=3.356970, loss_obj=17.064116, loss_cls=1.983199, loss=34.219208, lr=0.000125, time_each_step=0.78s, eta=0:53:19\n",
      "2022-02-22 16:45:01 [INFO]\t[TRAIN] Epoch=8/30, Step=145/175, loss_xy=14.903126, loss_wh=4.110880, loss_obj=20.208324, loss_cls=4.673831, loss=43.896160, lr=0.000125, time_each_step=0.82s, eta=0:55:21\n",
      "2022-02-22 16:45:07 [INFO]\t[TRAIN] Epoch=8/30, Step=155/175, loss_xy=13.673150, loss_wh=4.056454, loss_obj=19.082134, loss_cls=3.717875, loss=40.529613, lr=0.000125, time_each_step=0.66s, eta=0:44:54\n",
      "2022-02-22 16:45:14 [INFO]\t[TRAIN] Epoch=8/30, Step=165/175, loss_xy=10.498246, loss_wh=3.292653, loss_obj=15.850183, loss_cls=2.358572, loss=31.999655, lr=0.000125, time_each_step=0.66s, eta=0:44:51\n",
      "2022-02-22 16:45:22 [INFO]\t[TRAIN] Epoch=8/30, Step=175/175, loss_xy=7.618791, loss_wh=2.253730, loss_obj=11.637961, loss_cls=1.580178, loss=23.090660, lr=0.000125, time_each_step=0.79s, eta=0:52:57\n",
      "2022-02-22 16:45:22 [INFO]\t[TRAIN] Epoch 8 finished, loss_xy=12.050312, loss_wh=3.756674, loss_obj=18.197174, loss_cls=3.326617, loss=37.33078 .\n",
      "2022-02-22 16:45:31 [INFO]\t[TRAIN] Epoch=9/30, Step=10/175, loss_xy=13.737019, loss_wh=3.776679, loss_obj=18.535654, loss_cls=3.362904, loss=39.412258, lr=0.000125, time_each_step=0.88s, eta=0:58:50\n",
      "2022-02-22 16:45:38 [INFO]\t[TRAIN] Epoch=9/30, Step=20/175, loss_xy=10.542120, loss_wh=2.936217, loss_obj=16.757980, loss_cls=2.272408, loss=32.508724, lr=0.000125, time_each_step=0.71s, eta=0:47:39\n",
      "2022-02-22 16:45:45 [INFO]\t[TRAIN] Epoch=9/30, Step=30/175, loss_xy=14.356434, loss_wh=4.024125, loss_obj=21.839241, loss_cls=3.341749, loss=43.561546, lr=0.000125, time_each_step=0.69s, eta=0:46:8\n",
      "2022-02-22 16:45:53 [INFO]\t[TRAIN] Epoch=9/30, Step=40/175, loss_xy=11.663741, loss_wh=3.775661, loss_obj=16.839098, loss_cls=1.940407, loss=34.218906, lr=0.000125, time_each_step=0.83s, eta=0:55:0\n",
      "2022-02-22 16:46:00 [INFO]\t[TRAIN] Epoch=9/30, Step=50/175, loss_xy=11.470047, loss_wh=3.246107, loss_obj=13.667370, loss_cls=3.890127, loss=32.273651, lr=0.000125, time_each_step=0.69s, eta=0:46:23\n",
      "2022-02-22 16:46:06 [INFO]\t[TRAIN] Epoch=9/30, Step=60/175, loss_xy=15.937092, loss_wh=5.997226, loss_obj=21.228483, loss_cls=4.426956, loss=47.589760, lr=0.000125, time_each_step=0.62s, eta=0:41:49\n",
      "2022-02-22 16:46:13 [INFO]\t[TRAIN] Epoch=9/30, Step=70/175, loss_xy=12.007481, loss_wh=3.875435, loss_obj=16.921268, loss_cls=5.114841, loss=37.919025, lr=0.000125, time_each_step=0.72s, eta=0:47:46\n",
      "2022-02-22 16:46:21 [INFO]\t[TRAIN] Epoch=9/30, Step=80/175, loss_xy=11.867333, loss_wh=3.410745, loss_obj=18.738579, loss_cls=3.084636, loss=37.101295, lr=0.000125, time_each_step=0.77s, eta=0:50:53\n",
      "2022-02-22 16:46:28 [INFO]\t[TRAIN] Epoch=9/30, Step=90/175, loss_xy=10.258345, loss_wh=2.751587, loss_obj=15.480946, loss_cls=1.884476, loss=30.375353, lr=0.000125, time_each_step=0.67s, eta=0:44:42\n",
      "2022-02-22 16:46:35 [INFO]\t[TRAIN] Epoch=9/30, Step=100/175, loss_xy=10.362463, loss_wh=3.027553, loss_obj=16.092079, loss_cls=1.774427, loss=31.256521, lr=0.000125, time_each_step=0.7s, eta=0:46:1\n",
      "2022-02-22 16:46:42 [INFO]\t[TRAIN] Epoch=9/30, Step=110/175, loss_xy=11.163914, loss_wh=2.897602, loss_obj=16.637543, loss_cls=2.355462, loss=33.054520, lr=0.000125, time_each_step=0.7s, eta=0:46:11\n",
      "2022-02-22 16:46:49 [INFO]\t[TRAIN] Epoch=9/30, Step=120/175, loss_xy=9.957033, loss_wh=3.429950, loss_obj=15.319823, loss_cls=2.819952, loss=31.526758, lr=0.000125, time_each_step=0.69s, eta=0:45:12\n",
      "2022-02-22 16:46:56 [INFO]\t[TRAIN] Epoch=9/30, Step=130/175, loss_xy=10.280231, loss_wh=2.924161, loss_obj=15.765992, loss_cls=2.650867, loss=31.621250, lr=0.000125, time_each_step=0.71s, eta=0:46:25\n",
      "2022-02-22 16:47:03 [INFO]\t[TRAIN] Epoch=9/30, Step=140/175, loss_xy=12.404402, loss_wh=3.692918, loss_obj=18.374006, loss_cls=4.181974, loss=38.653297, lr=0.000125, time_each_step=0.71s, eta=0:46:17\n",
      "2022-02-22 16:47:09 [INFO]\t[TRAIN] Epoch=9/30, Step=150/175, loss_xy=13.380013, loss_wh=4.223205, loss_obj=21.177200, loss_cls=2.639446, loss=41.419865, lr=0.000125, time_each_step=0.67s, eta=0:43:31\n",
      "2022-02-22 16:47:18 [INFO]\t[TRAIN] Epoch=9/30, Step=160/175, loss_xy=11.809065, loss_wh=3.246619, loss_obj=17.135399, loss_cls=2.389227, loss=34.580311, lr=0.000125, time_each_step=0.82s, eta=0:52:43\n",
      "2022-02-22 16:47:24 [INFO]\t[TRAIN] Epoch=9/30, Step=170/175, loss_xy=9.996708, loss_wh=2.860467, loss_obj=14.722500, loss_cls=1.991983, loss=29.571659, lr=0.000125, time_each_step=0.67s, eta=0:43:23\n",
      "2022-02-22 16:47:28 [INFO]\t[TRAIN] Epoch 9 finished, loss_xy=12.124389, loss_wh=3.67192, loss_obj=17.617338, loss_cls=3.1380177, loss=36.551666 .\n",
      "2022-02-22 16:47:35 [INFO]\t[TRAIN] Epoch=10/30, Step=5/175, loss_xy=12.694803, loss_wh=3.496641, loss_obj=19.268705, loss_cls=2.657204, loss=38.117355, lr=0.000125, time_each_step=1.02s, eta=1:4:36\n",
      "2022-02-22 16:47:43 [INFO]\t[TRAIN] Epoch=10/30, Step=15/175, loss_xy=11.849302, loss_wh=3.623903, loss_obj=16.074158, loss_cls=1.757616, loss=33.304977, lr=0.000125, time_each_step=0.77s, eta=0:49:9\n",
      "2022-02-22 16:47:50 [INFO]\t[TRAIN] Epoch=10/30, Step=25/175, loss_xy=11.476418, loss_wh=3.308402, loss_obj=19.382366, loss_cls=2.975162, loss=37.142349, lr=0.000125, time_each_step=0.78s, eta=0:49:32\n",
      "2022-02-22 16:47:57 [INFO]\t[TRAIN] Epoch=10/30, Step=35/175, loss_xy=12.523392, loss_wh=3.190474, loss_obj=19.161770, loss_cls=2.778838, loss=37.654472, lr=0.000125, time_each_step=0.69s, eta=0:44:8\n",
      "2022-02-22 16:48:04 [INFO]\t[TRAIN] Epoch=10/30, Step=45/175, loss_xy=13.387782, loss_wh=4.451097, loss_obj=18.014027, loss_cls=2.545846, loss=38.398750, lr=0.000125, time_each_step=0.66s, eta=0:41:39\n",
      "2022-02-22 16:48:11 [INFO]\t[TRAIN] Epoch=10/30, Step=55/175, loss_xy=8.040205, loss_wh=2.611099, loss_obj=11.816347, loss_cls=2.036684, loss=24.504335, lr=0.000125, time_each_step=0.71s, eta=0:45:6\n",
      "2022-02-22 16:48:19 [INFO]\t[TRAIN] Epoch=10/30, Step=65/175, loss_xy=9.428240, loss_wh=2.469200, loss_obj=17.667830, loss_cls=2.706329, loss=32.271599, lr=0.000125, time_each_step=0.83s, eta=0:51:51\n",
      "2022-02-22 16:48:27 [INFO]\t[TRAIN] Epoch=10/30, Step=75/175, loss_xy=10.999317, loss_wh=2.886128, loss_obj=16.315372, loss_cls=2.374746, loss=32.575562, lr=0.000125, time_each_step=0.75s, eta=0:46:49\n",
      "2022-02-22 16:48:34 [INFO]\t[TRAIN] Epoch=10/30, Step=85/175, loss_xy=14.065722, loss_wh=4.225805, loss_obj=18.903769, loss_cls=3.695553, loss=40.890850, lr=0.000125, time_each_step=0.72s, eta=0:45:1\n",
      "2022-02-22 16:48:41 [INFO]\t[TRAIN] Epoch=10/30, Step=95/175, loss_xy=11.242381, loss_wh=3.792593, loss_obj=14.257480, loss_cls=1.677319, loss=30.969772, lr=0.000125, time_each_step=0.71s, eta=0:44:29\n",
      "2022-02-22 16:48:49 [INFO]\t[TRAIN] Epoch=10/30, Step=105/175, loss_xy=9.258184, loss_wh=2.842905, loss_obj=16.859760, loss_cls=2.610480, loss=31.571331, lr=0.000125, time_each_step=0.76s, eta=0:47:2\n",
      "2022-02-22 16:48:55 [INFO]\t[TRAIN] Epoch=10/30, Step=115/175, loss_xy=11.018581, loss_wh=3.304708, loss_obj=15.958864, loss_cls=1.872282, loss=32.154434, lr=0.000125, time_each_step=0.62s, eta=0:39:1\n",
      "2022-02-22 16:49:02 [INFO]\t[TRAIN] Epoch=10/30, Step=125/175, loss_xy=13.205729, loss_wh=3.496857, loss_obj=17.498804, loss_cls=3.474493, loss=37.675888, lr=0.000125, time_each_step=0.74s, eta=0:45:54\n",
      "2022-02-22 16:49:11 [INFO]\t[TRAIN] Epoch=10/30, Step=135/175, loss_xy=13.069460, loss_wh=3.495401, loss_obj=16.950840, loss_cls=2.999004, loss=36.514706, lr=0.000125, time_each_step=0.87s, eta=0:53:20\n",
      "2022-02-22 16:49:20 [INFO]\t[TRAIN] Epoch=10/30, Step=145/175, loss_xy=10.193213, loss_wh=2.524393, loss_obj=13.901917, loss_cls=2.182197, loss=28.801720, lr=0.000125, time_each_step=0.87s, eta=0:53:7\n",
      "2022-02-22 16:49:26 [INFO]\t[TRAIN] Epoch=10/30, Step=155/175, loss_xy=13.181648, loss_wh=3.828216, loss_obj=17.989071, loss_cls=3.098775, loss=38.097710, lr=0.000125, time_each_step=0.61s, eta=0:37:56\n",
      "2022-02-22 16:49:33 [INFO]\t[TRAIN] Epoch=10/30, Step=165/175, loss_xy=10.360983, loss_wh=3.056213, loss_obj=14.259851, loss_cls=2.758341, loss=30.435389, lr=0.000125, time_each_step=0.71s, eta=0:43:43\n",
      "2022-02-22 16:49:41 [INFO]\t[TRAIN] Epoch=10/30, Step=175/175, loss_xy=13.355369, loss_wh=3.021426, loss_obj=14.548876, loss_cls=3.832420, loss=34.758091, lr=0.000125, time_each_step=0.8s, eta=0:48:37\n",
      "2022-02-22 16:49:41 [INFO]\t[TRAIN] Epoch 10 finished, loss_xy=12.04839, loss_wh=3.5783284, loss_obj=17.28223, loss_cls=2.8944824, loss=35.803432 .\n",
      "2022-02-22 16:49:41 [WARNING]\tDetector only supports single card evaluation with batch_size=1 during evaluation, so batch_size is forcibly set to 1.\n",
      "2022-02-22 16:49:42 [INFO]\tStart to evaluate(total_samples=1000, total_steps=1000)...\n",
      "2022-02-22 16:50:10 [INFO]\tAccumulating evaluatation results...\n",
      "2022-02-22 16:50:10 [INFO]\t[EVAL] Finished, Epoch=10, bbox_map=52.866977 .\n",
      "2022-02-22 16:50:12 [INFO]\tModel saved in output/yolov3_darknet53/best_model.\n",
      "2022-02-22 16:50:12 [INFO]\tCurrent evaluated best model on eval_dataset is epoch_10, bbox_map=52.866976879893116\n",
      "2022-02-22 16:50:14 [INFO]\tModel saved in output/yolov3_darknet53/epoch_10.\n",
      "2022-02-22 16:50:23 [INFO]\t[TRAIN] Epoch=11/30, Step=10/175, loss_xy=13.817553, loss_wh=4.020777, loss_obj=17.895454, loss_cls=3.697818, loss=39.431602, lr=0.000125, time_each_step=0.93s, eta=0:56:26\n",
      "2022-02-22 16:50:31 [INFO]\t[TRAIN] Epoch=11/30, Step=20/175, loss_xy=12.439060, loss_wh=4.025863, loss_obj=18.681440, loss_cls=2.515163, loss=37.661526, lr=0.000125, time_each_step=0.74s, eta=0:44:57\n",
      "2022-02-22 16:50:38 [INFO]\t[TRAIN] Epoch=11/30, Step=30/175, loss_xy=13.425976, loss_wh=4.162775, loss_obj=19.410940, loss_cls=2.930050, loss=39.929741, lr=0.000125, time_each_step=0.77s, eta=0:46:45\n",
      "2022-02-22 16:50:46 [INFO]\t[TRAIN] Epoch=11/30, Step=40/175, loss_xy=11.925393, loss_wh=3.785603, loss_obj=15.676718, loss_cls=3.325858, loss=34.713573, lr=0.000125, time_each_step=0.77s, eta=0:46:29\n",
      "2022-02-22 16:50:53 [INFO]\t[TRAIN] Epoch=11/30, Step=50/175, loss_xy=11.359628, loss_wh=2.779624, loss_obj=15.803658, loss_cls=2.396449, loss=32.339359, lr=0.000125, time_each_step=0.72s, eta=0:43:26\n",
      "2022-02-22 16:50:59 [INFO]\t[TRAIN] Epoch=11/30, Step=60/175, loss_xy=10.298564, loss_wh=3.339711, loss_obj=13.765247, loss_cls=2.148109, loss=29.551632, lr=0.000125, time_each_step=0.59s, eta=0:35:48\n",
      "2022-02-22 16:51:06 [INFO]\t[TRAIN] Epoch=11/30, Step=70/175, loss_xy=10.694710, loss_wh=3.050530, loss_obj=14.183149, loss_cls=2.127610, loss=30.056002, lr=0.000125, time_each_step=0.72s, eta=0:43:19\n",
      "2022-02-22 16:51:13 [INFO]\t[TRAIN] Epoch=11/30, Step=80/175, loss_xy=11.345608, loss_wh=2.941262, loss_obj=14.484615, loss_cls=2.236400, loss=31.007885, lr=0.000125, time_each_step=0.7s, eta=0:41:43\n",
      "2022-02-22 16:51:22 [INFO]\t[TRAIN] Epoch=11/30, Step=90/175, loss_xy=11.084676, loss_wh=3.153613, loss_obj=13.844211, loss_cls=3.337540, loss=31.420040, lr=0.000125, time_each_step=0.84s, eta=0:49:37\n",
      "2022-02-22 16:51:30 [INFO]\t[TRAIN] Epoch=11/30, Step=100/175, loss_xy=11.096167, loss_wh=2.971713, loss_obj=16.243828, loss_cls=2.431736, loss=32.743443, lr=0.000125, time_each_step=0.89s, eta=0:52:36\n",
      "2022-02-22 16:51:39 [INFO]\t[TRAIN] Epoch=11/30, Step=110/175, loss_xy=15.878292, loss_wh=4.504020, loss_obj=25.624897, loss_cls=2.382489, loss=48.389698, lr=0.000125, time_each_step=0.88s, eta=0:51:59\n",
      "2022-02-22 16:51:46 [INFO]\t[TRAIN] Epoch=11/30, Step=120/175, loss_xy=14.047946, loss_wh=3.899374, loss_obj=19.975395, loss_cls=2.792941, loss=40.715656, lr=0.000125, time_each_step=0.68s, eta=0:40:38\n",
      "2022-02-22 16:51:52 [INFO]\t[TRAIN] Epoch=11/30, Step=130/175, loss_xy=11.013828, loss_wh=3.238315, loss_obj=17.502930, loss_cls=1.602790, loss=33.357864, lr=0.000125, time_each_step=0.6s, eta=0:35:42\n",
      "2022-02-22 16:52:00 [INFO]\t[TRAIN] Epoch=11/30, Step=140/175, loss_xy=14.500163, loss_wh=4.264143, loss_obj=18.985970, loss_cls=3.219823, loss=40.970097, lr=0.000125, time_each_step=0.77s, eta=0:45:26\n",
      "2022-02-22 16:52:07 [INFO]\t[TRAIN] Epoch=11/30, Step=150/175, loss_xy=10.695267, loss_wh=2.965702, loss_obj=16.698406, loss_cls=2.795570, loss=33.154945, lr=0.000125, time_each_step=0.73s, eta=0:43:1\n",
      "2022-02-22 16:52:14 [INFO]\t[TRAIN] Epoch=11/30, Step=160/175, loss_xy=10.872617, loss_wh=3.249117, loss_obj=14.622101, loss_cls=1.674168, loss=30.418003, lr=0.000125, time_each_step=0.7s, eta=0:40:55\n",
      "2022-02-22 16:52:22 [INFO]\t[TRAIN] Epoch=11/30, Step=170/175, loss_xy=16.144226, loss_wh=4.462130, loss_obj=21.691525, loss_cls=4.066632, loss=46.364513, lr=0.000125, time_each_step=0.77s, eta=0:44:36\n",
      "2022-02-22 16:52:25 [INFO]\t[TRAIN] Epoch 11 finished, loss_xy=12.094615, loss_wh=3.4478745, loss_obj=17.12659, loss_cls=2.8085742, loss=35.477654 .\n",
      "2022-02-22 16:52:30 [INFO]\t[TRAIN] Epoch=12/30, Step=5/175, loss_xy=12.317583, loss_wh=4.312215, loss_obj=18.781652, loss_cls=2.736267, loss=38.147720, lr=0.000125, time_each_step=0.81s, eta=0:46:54\n",
      "2022-02-22 16:52:37 [INFO]\t[TRAIN] Epoch=12/30, Step=15/175, loss_xy=11.819051, loss_wh=3.161788, loss_obj=17.567984, loss_cls=2.804316, loss=35.353138, lr=0.000125, time_each_step=0.73s, eta=0:42:18\n",
      "2022-02-22 16:52:45 [INFO]\t[TRAIN] Epoch=12/30, Step=25/175, loss_xy=9.068860, loss_wh=2.791195, loss_obj=12.934523, loss_cls=2.424578, loss=27.219156, lr=0.000125, time_each_step=0.73s, eta=0:42:20\n",
      "2022-02-22 16:52:51 [INFO]\t[TRAIN] Epoch=12/30, Step=35/175, loss_xy=9.589262, loss_wh=2.542694, loss_obj=12.228198, loss_cls=1.974986, loss=26.335138, lr=0.000125, time_each_step=0.67s, eta=0:38:33\n",
      "2022-02-22 16:52:59 [INFO]\t[TRAIN] Epoch=12/30, Step=45/175, loss_xy=12.743996, loss_wh=3.829566, loss_obj=17.010590, loss_cls=3.393267, loss=36.977421, lr=0.000125, time_each_step=0.75s, eta=0:43:10\n",
      "2022-02-22 16:53:06 [INFO]\t[TRAIN] Epoch=12/30, Step=55/175, loss_xy=11.335783, loss_wh=3.210629, loss_obj=18.268187, loss_cls=3.274174, loss=36.088772, lr=0.000125, time_each_step=0.72s, eta=0:41:12\n",
      "2022-02-22 16:53:14 [INFO]\t[TRAIN] Epoch=12/30, Step=65/175, loss_xy=13.010302, loss_wh=3.426884, loss_obj=18.250872, loss_cls=3.789693, loss=38.477749, lr=0.000125, time_each_step=0.8s, eta=0:45:34\n",
      "2022-02-22 16:53:21 [INFO]\t[TRAIN] Epoch=12/30, Step=75/175, loss_xy=15.782372, loss_wh=3.708828, loss_obj=21.472370, loss_cls=2.836928, loss=43.800499, lr=0.000125, time_each_step=0.73s, eta=0:41:34\n",
      "2022-02-22 16:53:28 [INFO]\t[TRAIN] Epoch=12/30, Step=85/175, loss_xy=11.088434, loss_wh=3.204420, loss_obj=16.160131, loss_cls=4.003183, loss=34.456169, lr=0.000125, time_each_step=0.62s, eta=0:35:43\n",
      "2022-02-22 16:53:36 [INFO]\t[TRAIN] Epoch=12/30, Step=95/175, loss_xy=9.991482, loss_wh=2.563441, loss_obj=15.962199, loss_cls=2.160016, loss=30.677135, lr=0.000125, time_each_step=0.8s, eta=0:45:7\n",
      "2022-02-22 16:53:43 [INFO]\t[TRAIN] Epoch=12/30, Step=105/175, loss_xy=12.944044, loss_wh=3.660551, loss_obj=19.402603, loss_cls=3.609637, loss=39.616837, lr=0.000125, time_each_step=0.73s, eta=0:41:18\n",
      "2022-02-22 16:53:51 [INFO]\t[TRAIN] Epoch=12/30, Step=115/175, loss_xy=9.327659, loss_wh=2.434762, loss_obj=14.349657, loss_cls=1.831878, loss=27.943956, lr=0.000125, time_each_step=0.78s, eta=0:43:54\n",
      "2022-02-22 16:53:58 [INFO]\t[TRAIN] Epoch=12/30, Step=125/175, loss_xy=12.392543, loss_wh=3.979990, loss_obj=17.175699, loss_cls=2.674775, loss=36.223007, lr=0.000125, time_each_step=0.69s, eta=0:39:2\n",
      "2022-02-22 16:54:05 [INFO]\t[TRAIN] Epoch=12/30, Step=135/175, loss_xy=11.824066, loss_wh=3.149394, loss_obj=18.463573, loss_cls=3.321308, loss=36.758343, lr=0.000125, time_each_step=0.76s, eta=0:42:44\n",
      "2022-02-22 16:54:12 [INFO]\t[TRAIN] Epoch=12/30, Step=145/175, loss_xy=8.932819, loss_wh=2.528989, loss_obj=14.516889, loss_cls=1.622247, loss=27.600945, lr=0.000125, time_each_step=0.65s, eta=0:36:21\n",
      "2022-02-22 16:54:19 [INFO]\t[TRAIN] Epoch=12/30, Step=155/175, loss_xy=9.487791, loss_wh=2.568991, loss_obj=12.722214, loss_cls=1.510204, loss=26.289200, lr=0.000125, time_each_step=0.68s, eta=0:37:53\n",
      "2022-02-22 16:54:27 [INFO]\t[TRAIN] Epoch=12/30, Step=165/175, loss_xy=10.437225, loss_wh=2.616697, loss_obj=13.919056, loss_cls=0.975101, loss=27.948080, lr=0.000125, time_each_step=0.84s, eta=0:46:9\n",
      "2022-02-22 16:54:35 [INFO]\t[TRAIN] Epoch=12/30, Step=175/175, loss_xy=12.055483, loss_wh=3.154959, loss_obj=17.027788, loss_cls=2.677851, loss=34.916077, lr=0.000125, time_each_step=0.76s, eta=0:42:6\n",
      "2022-02-22 16:54:35 [INFO]\t[TRAIN] Epoch 12 finished, loss_xy=11.935803, loss_wh=3.307576, loss_obj=16.65223, loss_cls=2.6776898, loss=34.573296 .\n",
      "2022-02-22 16:54:43 [INFO]\t[TRAIN] Epoch=13/30, Step=10/175, loss_xy=12.154671, loss_wh=3.994370, loss_obj=16.062366, loss_cls=1.932951, loss=34.144356, lr=0.000125, time_each_step=0.78s, eta=0:43:9\n",
      "2022-02-22 16:54:52 [INFO]\t[TRAIN] Epoch=13/30, Step=20/175, loss_xy=11.885069, loss_wh=3.388526, loss_obj=17.324886, loss_cls=2.693388, loss=35.291866, lr=0.000125, time_each_step=0.98s, eta=0:52:59\n",
      "2022-02-22 16:54:59 [INFO]\t[TRAIN] Epoch=13/30, Step=30/175, loss_xy=11.470640, loss_wh=3.120611, loss_obj=15.933968, loss_cls=2.770326, loss=33.295544, lr=0.000125, time_each_step=0.69s, eta=0:37:52\n",
      "2022-02-22 16:55:07 [INFO]\t[TRAIN] Epoch=13/30, Step=40/175, loss_xy=14.836268, loss_wh=4.447885, loss_obj=19.088879, loss_cls=2.637741, loss=41.010773, lr=0.000125, time_each_step=0.75s, eta=0:40:48\n",
      "2022-02-22 16:55:14 [INFO]\t[TRAIN] Epoch=13/30, Step=50/175, loss_xy=12.177903, loss_wh=3.270970, loss_obj=17.676392, loss_cls=2.108019, loss=35.233288, lr=0.000125, time_each_step=0.77s, eta=0:42:2\n",
      "2022-02-22 16:55:26 [INFO]\t[TRAIN] Epoch=13/30, Step=60/175, loss_xy=10.340484, loss_wh=2.677474, loss_obj=15.332958, loss_cls=2.605989, loss=30.956905, lr=0.000125, time_each_step=1.14s, eta=1:0:40\n",
      "2022-02-22 16:55:34 [INFO]\t[TRAIN] Epoch=13/30, Step=70/175, loss_xy=14.544957, loss_wh=3.940030, loss_obj=20.919374, loss_cls=1.793359, loss=41.197720, lr=0.000125, time_each_step=0.77s, eta=0:41:32\n",
      "2022-02-22 16:55:41 [INFO]\t[TRAIN] Epoch=13/30, Step=80/175, loss_xy=10.069618, loss_wh=3.145478, loss_obj=12.675655, loss_cls=2.199220, loss=28.089970, lr=0.000125, time_each_step=0.78s, eta=0:41:52\n",
      "2022-02-22 16:55:49 [INFO]\t[TRAIN] Epoch=13/30, Step=90/175, loss_xy=12.517859, loss_wh=3.209975, loss_obj=16.879658, loss_cls=3.500806, loss=36.108295, lr=0.000125, time_each_step=0.73s, eta=0:39:19\n",
      "2022-02-22 16:55:56 [INFO]\t[TRAIN] Epoch=13/30, Step=100/175, loss_xy=12.606087, loss_wh=3.544370, loss_obj=17.602709, loss_cls=2.641346, loss=36.394512, lr=0.000125, time_each_step=0.71s, eta=0:37:58\n",
      "2022-02-22 16:56:03 [INFO]\t[TRAIN] Epoch=13/30, Step=110/175, loss_xy=12.322674, loss_wh=3.727490, loss_obj=21.025497, loss_cls=2.404821, loss=39.480484, lr=0.000125, time_each_step=0.74s, eta=0:39:32\n",
      "2022-02-22 16:56:10 [INFO]\t[TRAIN] Epoch=13/30, Step=120/175, loss_xy=11.282681, loss_wh=3.125009, loss_obj=16.950134, loss_cls=2.217034, loss=33.574860, lr=0.000125, time_each_step=0.72s, eta=0:38:21\n",
      "2022-02-22 16:56:16 [INFO]\t[TRAIN] Epoch=13/30, Step=130/175, loss_xy=11.185835, loss_wh=3.388998, loss_obj=15.001986, loss_cls=3.178003, loss=32.754822, lr=0.000125, time_each_step=0.61s, eta=0:32:58\n",
      "2022-02-22 16:56:24 [INFO]\t[TRAIN] Epoch=13/30, Step=140/175, loss_xy=13.449791, loss_wh=3.553289, loss_obj=19.391020, loss_cls=2.915015, loss=39.309116, lr=0.000125, time_each_step=0.76s, eta=0:39:59\n",
      "2022-02-22 16:56:32 [INFO]\t[TRAIN] Epoch=13/30, Step=150/175, loss_xy=8.763136, loss_wh=2.242295, loss_obj=13.165497, loss_cls=1.835771, loss=26.006701, lr=0.000125, time_each_step=0.76s, eta=0:40:18\n",
      "2022-02-22 16:56:40 [INFO]\t[TRAIN] Epoch=13/30, Step=160/175, loss_xy=14.332886, loss_wh=3.443629, loss_obj=23.606525, loss_cls=2.385988, loss=43.769028, lr=0.000125, time_each_step=0.79s, eta=0:41:32\n",
      "2022-02-22 16:56:47 [INFO]\t[TRAIN] Epoch=13/30, Step=170/175, loss_xy=12.770459, loss_wh=3.747158, loss_obj=19.130976, loss_cls=2.975528, loss=38.624119, lr=0.000125, time_each_step=0.74s, eta=0:38:51\n",
      "2022-02-22 16:56:50 [INFO]\t[TRAIN] Epoch 13 finished, loss_xy=11.803186, loss_wh=3.285609, loss_obj=16.384495, loss_cls=2.5188155, loss=33.992107 .\n",
      "2022-02-22 16:56:56 [INFO]\t[TRAIN] Epoch=14/30, Step=5/175, loss_xy=11.690405, loss_wh=3.663289, loss_obj=16.910452, loss_cls=3.508566, loss=35.772709, lr=0.000125, time_each_step=0.83s, eta=0:43:24\n",
      "2022-02-22 16:57:03 [INFO]\t[TRAIN] Epoch=14/30, Step=15/175, loss_xy=12.974120, loss_wh=3.228928, loss_obj=17.693539, loss_cls=2.411552, loss=36.308140, lr=0.000125, time_each_step=0.77s, eta=0:40:4\n",
      "2022-02-22 16:57:10 [INFO]\t[TRAIN] Epoch=14/30, Step=25/175, loss_xy=9.838086, loss_wh=2.591847, loss_obj=13.253815, loss_cls=2.818746, loss=28.502495, lr=0.000125, time_each_step=0.7s, eta=0:36:39\n",
      "2022-02-22 16:57:18 [INFO]\t[TRAIN] Epoch=14/30, Step=35/175, loss_xy=10.507936, loss_wh=3.283021, loss_obj=14.889586, loss_cls=1.805612, loss=30.486155, lr=0.000125, time_each_step=0.72s, eta=0:37:28\n",
      "2022-02-22 16:57:25 [INFO]\t[TRAIN] Epoch=14/30, Step=45/175, loss_xy=9.723973, loss_wh=2.983276, loss_obj=11.309990, loss_cls=2.531653, loss=26.548891, lr=0.000125, time_each_step=0.71s, eta=0:36:56\n",
      "2022-02-22 16:57:33 [INFO]\t[TRAIN] Epoch=14/30, Step=55/175, loss_xy=13.271029, loss_wh=3.288218, loss_obj=16.459009, loss_cls=1.851064, loss=34.869320, lr=0.000125, time_each_step=0.86s, eta=0:43:48\n",
      "2022-02-22 16:57:40 [INFO]\t[TRAIN] Epoch=14/30, Step=65/175, loss_xy=11.388147, loss_wh=2.818270, loss_obj=12.760353, loss_cls=1.842679, loss=28.809448, lr=0.000125, time_each_step=0.65s, eta=0:33:23\n",
      "2022-02-22 16:57:47 [INFO]\t[TRAIN] Epoch=14/30, Step=75/175, loss_xy=14.444237, loss_wh=4.211055, loss_obj=18.041710, loss_cls=3.551911, loss=40.248913, lr=0.000125, time_each_step=0.77s, eta=0:39:4\n",
      "2022-02-22 16:57:54 [INFO]\t[TRAIN] Epoch=14/30, Step=85/175, loss_xy=15.499514, loss_wh=4.188532, loss_obj=21.581367, loss_cls=3.096488, loss=44.365902, lr=0.000125, time_each_step=0.71s, eta=0:36:16\n",
      "2022-02-22 16:58:01 [INFO]\t[TRAIN] Epoch=14/30, Step=95/175, loss_xy=11.983613, loss_wh=2.957415, loss_obj=15.266722, loss_cls=2.526190, loss=32.733940, lr=0.000125, time_each_step=0.64s, eta=0:32:54\n",
      "2022-02-22 16:58:10 [INFO]\t[TRAIN] Epoch=14/30, Step=105/175, loss_xy=12.675606, loss_wh=3.338422, loss_obj=19.282478, loss_cls=3.658226, loss=38.954731, lr=0.000125, time_each_step=0.88s, eta=0:43:56\n",
      "2022-02-22 16:58:16 [INFO]\t[TRAIN] Epoch=14/30, Step=115/175, loss_xy=11.428984, loss_wh=3.378158, loss_obj=13.787251, loss_cls=1.515571, loss=30.109962, lr=0.000125, time_each_step=0.67s, eta=0:33:58\n",
      "2022-02-22 16:58:23 [INFO]\t[TRAIN] Epoch=14/30, Step=125/175, loss_xy=13.932676, loss_wh=4.015414, loss_obj=18.443892, loss_cls=2.159067, loss=38.551048, lr=0.000125, time_each_step=0.71s, eta=0:35:56\n",
      "2022-02-22 16:58:31 [INFO]\t[TRAIN] Epoch=14/30, Step=135/175, loss_xy=12.241879, loss_wh=2.826336, loss_obj=16.704288, loss_cls=1.595708, loss=33.368210, lr=0.000125, time_each_step=0.79s, eta=0:39:15\n",
      "2022-02-22 16:58:38 [INFO]\t[TRAIN] Epoch=14/30, Step=145/175, loss_xy=12.584671, loss_wh=4.778452, loss_obj=15.228834, loss_cls=1.771701, loss=34.363659, lr=0.000125, time_each_step=0.67s, eta=0:33:34\n",
      "2022-02-22 16:58:46 [INFO]\t[TRAIN] Epoch=14/30, Step=155/175, loss_xy=10.651196, loss_wh=2.548617, loss_obj=13.954882, loss_cls=1.724127, loss=28.878822, lr=0.000125, time_each_step=0.81s, eta=0:40:3\n",
      "2022-02-22 16:58:54 [INFO]\t[TRAIN] Epoch=14/30, Step=165/175, loss_xy=12.803413, loss_wh=2.807853, loss_obj=16.257561, loss_cls=2.419795, loss=34.288624, lr=0.000125, time_each_step=0.77s, eta=0:38:12\n",
      "2022-02-22 16:59:03 [INFO]\t[TRAIN] Epoch=14/30, Step=175/175, loss_xy=13.584555, loss_wh=3.695383, loss_obj=17.056946, loss_cls=3.507616, loss=37.844498, lr=0.000125, time_each_step=0.88s, eta=0:43:22\n",
      "2022-02-22 16:59:03 [INFO]\t[TRAIN] Epoch 14 finished, loss_xy=11.917369, loss_wh=3.294439, loss_obj=16.237507, loss_cls=2.4837255, loss=33.93304 .\n",
      "2022-02-22 16:59:12 [INFO]\t[TRAIN] Epoch=15/30, Step=10/175, loss_xy=12.799284, loss_wh=3.591932, loss_obj=18.659233, loss_cls=1.651023, loss=36.701473, lr=0.000125, time_each_step=0.95s, eta=0:45:41\n",
      "2022-02-22 16:59:20 [INFO]\t[TRAIN] Epoch=15/30, Step=20/175, loss_xy=12.430025, loss_wh=3.746914, loss_obj=16.289660, loss_cls=2.766737, loss=35.233337, lr=0.000125, time_each_step=0.75s, eta=0:36:30\n",
      "2022-02-22 16:59:27 [INFO]\t[TRAIN] Epoch=15/30, Step=30/175, loss_xy=12.859581, loss_wh=3.777976, loss_obj=18.808514, loss_cls=3.973316, loss=39.419384, lr=0.000125, time_each_step=0.76s, eta=0:36:32\n",
      "2022-02-22 16:59:35 [INFO]\t[TRAIN] Epoch=15/30, Step=40/175, loss_xy=11.502424, loss_wh=2.979896, loss_obj=13.103424, loss_cls=2.416931, loss=30.002676, lr=0.000125, time_each_step=0.79s, eta=0:38:0\n",
      "2022-02-22 16:59:45 [INFO]\t[TRAIN] Epoch=15/30, Step=50/175, loss_xy=10.983481, loss_wh=2.670734, loss_obj=14.923443, loss_cls=1.832978, loss=30.410637, lr=0.000125, time_each_step=0.93s, eta=0:44:3\n",
      "2022-02-22 16:59:52 [INFO]\t[TRAIN] Epoch=15/30, Step=60/175, loss_xy=11.827100, loss_wh=3.324124, loss_obj=16.288372, loss_cls=3.650235, loss=35.089832, lr=0.000125, time_each_step=0.71s, eta=0:33:59\n",
      "2022-02-22 16:59:58 [INFO]\t[TRAIN] Epoch=15/30, Step=70/175, loss_xy=13.305275, loss_wh=3.583203, loss_obj=18.705976, loss_cls=2.852038, loss=38.446491, lr=0.000125, time_each_step=0.67s, eta=0:32:12\n",
      "2022-02-22 17:00:07 [INFO]\t[TRAIN] Epoch=15/30, Step=80/175, loss_xy=14.650866, loss_wh=3.909671, loss_obj=18.059267, loss_cls=2.294494, loss=38.914299, lr=0.000125, time_each_step=0.83s, eta=0:39:13\n",
      "2022-02-22 17:00:13 [INFO]\t[TRAIN] Epoch=15/30, Step=90/175, loss_xy=11.841864, loss_wh=4.204829, loss_obj=15.768417, loss_cls=2.338951, loss=34.154060, lr=0.000125, time_each_step=0.66s, eta=0:31:30\n",
      "2022-02-22 17:00:20 [INFO]\t[TRAIN] Epoch=15/30, Step=100/175, loss_xy=13.675882, loss_wh=4.301366, loss_obj=15.419691, loss_cls=2.735264, loss=36.132206, lr=0.000125, time_each_step=0.7s, eta=0:32:55\n",
      "2022-02-22 17:00:27 [INFO]\t[TRAIN] Epoch=15/30, Step=110/175, loss_xy=10.980329, loss_wh=2.418541, loss_obj=13.604012, loss_cls=2.052943, loss=29.055822, lr=0.000125, time_each_step=0.7s, eta=0:32:55\n",
      "2022-02-22 17:00:36 [INFO]\t[TRAIN] Epoch=15/30, Step=120/175, loss_xy=12.043030, loss_wh=2.912420, loss_obj=14.420162, loss_cls=1.731782, loss=31.107393, lr=0.000125, time_each_step=0.84s, eta=0:39:12\n",
      "2022-02-22 17:00:47 [INFO]\t[TRAIN] Epoch=15/30, Step=130/175, loss_xy=11.709321, loss_wh=3.222690, loss_obj=16.502024, loss_cls=1.844101, loss=33.278133, lr=0.000125, time_each_step=1.12s, eta=0:51:23\n",
      "2022-02-22 17:00:54 [INFO]\t[TRAIN] Epoch=15/30, Step=140/175, loss_xy=11.089051, loss_wh=3.069792, loss_obj=17.231514, loss_cls=2.519283, loss=33.909641, lr=0.000125, time_each_step=0.68s, eta=0:31:36\n",
      "2022-02-22 17:01:03 [INFO]\t[TRAIN] Epoch=15/30, Step=150/175, loss_xy=15.983295, loss_wh=4.655575, loss_obj=25.967680, loss_cls=4.282377, loss=50.888931, lr=0.000125, time_each_step=0.88s, eta=0:40:20\n",
      "2022-02-22 17:01:09 [INFO]\t[TRAIN] Epoch=15/30, Step=160/175, loss_xy=12.589959, loss_wh=3.434021, loss_obj=16.917089, loss_cls=3.197758, loss=36.138828, lr=0.000125, time_each_step=0.69s, eta=0:31:42\n",
      "2022-02-22 17:01:17 [INFO]\t[TRAIN] Epoch=15/30, Step=170/175, loss_xy=11.138126, loss_wh=2.858891, loss_obj=13.089531, loss_cls=1.268036, loss=28.354584, lr=0.000125, time_each_step=0.77s, eta=0:35:31\n",
      "2022-02-22 17:01:20 [INFO]\t[TRAIN] Epoch 15 finished, loss_xy=11.846845, loss_wh=3.1279893, loss_obj=16.144913, loss_cls=2.43285, loss=33.552597 .\n",
      "2022-02-22 17:01:20 [WARNING]\tDetector only supports single card evaluation with batch_size=1 during evaluation, so batch_size is forcibly set to 1.\n",
      "2022-02-22 17:01:20 [INFO]\tStart to evaluate(total_samples=1000, total_steps=1000)...\n",
      "2022-02-22 17:01:47 [INFO]\tAccumulating evaluatation results...\n",
      "2022-02-22 17:01:47 [INFO]\t[EVAL] Finished, Epoch=15, bbox_map=55.897664 .\n",
      "2022-02-22 17:01:49 [INFO]\tModel saved in output/yolov3_darknet53/best_model.\n",
      "2022-02-22 17:01:49 [INFO]\tCurrent evaluated best model on eval_dataset is epoch_15, bbox_map=55.897664393537774\n",
      "2022-02-22 17:01:50 [INFO]\tModel saved in output/yolov3_darknet53/epoch_15.\n",
      "2022-02-22 17:01:56 [INFO]\t[TRAIN] Epoch=16/30, Step=5/175, loss_xy=11.350353, loss_wh=3.936266, loss_obj=14.790600, loss_cls=2.587423, loss=32.664642, lr=0.000125, time_each_step=0.81s, eta=0:36:56\n",
      "2022-02-22 17:02:03 [INFO]\t[TRAIN] Epoch=16/30, Step=15/175, loss_xy=15.190066, loss_wh=4.426522, loss_obj=16.228407, loss_cls=4.041678, loss=39.886673, lr=0.000125, time_each_step=0.72s, eta=0:32:35\n",
      "2022-02-22 17:02:10 [INFO]\t[TRAIN] Epoch=16/30, Step=25/175, loss_xy=11.959112, loss_wh=2.882077, loss_obj=14.316855, loss_cls=1.582546, loss=30.740589, lr=0.000125, time_each_step=0.71s, eta=0:32:14\n",
      "2022-02-22 17:02:17 [INFO]\t[TRAIN] Epoch=16/30, Step=35/175, loss_xy=10.504538, loss_wh=2.554189, loss_obj=15.848358, loss_cls=1.280766, loss=30.187851, lr=0.000125, time_each_step=0.7s, eta=0:31:43\n",
      "2022-02-22 17:02:24 [INFO]\t[TRAIN] Epoch=16/30, Step=45/175, loss_xy=11.260462, loss_wh=2.350922, loss_obj=15.920416, loss_cls=1.066756, loss=30.598555, lr=0.000125, time_each_step=0.69s, eta=0:31:17\n",
      "2022-02-22 17:02:31 [INFO]\t[TRAIN] Epoch=16/30, Step=55/175, loss_xy=11.019009, loss_wh=3.181650, loss_obj=14.079917, loss_cls=1.316674, loss=29.597248, lr=0.000125, time_each_step=0.69s, eta=0:31:7\n",
      "2022-02-22 17:02:39 [INFO]\t[TRAIN] Epoch=16/30, Step=65/175, loss_xy=14.489899, loss_wh=3.985280, loss_obj=19.995758, loss_cls=2.755979, loss=41.226913, lr=0.000125, time_each_step=0.74s, eta=0:33:12\n",
      "2022-02-22 17:02:46 [INFO]\t[TRAIN] Epoch=16/30, Step=75/175, loss_xy=9.635019, loss_wh=2.570306, loss_obj=15.833341, loss_cls=1.995100, loss=30.033766, lr=0.000125, time_each_step=0.77s, eta=0:34:7\n",
      "2022-02-22 17:02:53 [INFO]\t[TRAIN] Epoch=16/30, Step=85/175, loss_xy=10.550855, loss_wh=2.652937, loss_obj=14.215473, loss_cls=1.661463, loss=29.080729, lr=0.000125, time_each_step=0.67s, eta=0:29:50\n",
      "2022-02-22 17:03:00 [INFO]\t[TRAIN] Epoch=16/30, Step=95/175, loss_xy=11.545696, loss_wh=3.242862, loss_obj=15.784611, loss_cls=3.222669, loss=33.795837, lr=0.000125, time_each_step=0.72s, eta=0:31:39\n",
      "2022-02-22 17:03:08 [INFO]\t[TRAIN] Epoch=16/30, Step=105/175, loss_xy=13.618582, loss_wh=3.422935, loss_obj=18.292248, loss_cls=1.611580, loss=36.945343, lr=0.000125, time_each_step=0.79s, eta=0:34:38\n",
      "2022-02-22 17:03:15 [INFO]\t[TRAIN] Epoch=16/30, Step=115/175, loss_xy=12.838758, loss_wh=3.362014, loss_obj=16.681194, loss_cls=2.020472, loss=34.902439, lr=0.000125, time_each_step=0.66s, eta=0:28:52\n",
      "2022-02-22 17:03:21 [INFO]\t[TRAIN] Epoch=16/30, Step=125/175, loss_xy=10.085479, loss_wh=2.816853, loss_obj=11.439098, loss_cls=1.822234, loss=26.163666, lr=0.000125, time_each_step=0.68s, eta=0:29:37\n",
      "2022-02-22 17:03:29 [INFO]\t[TRAIN] Epoch=16/30, Step=135/175, loss_xy=10.347666, loss_wh=2.465024, loss_obj=15.570042, loss_cls=1.494161, loss=29.876894, lr=0.000125, time_each_step=0.72s, eta=0:31:14\n",
      "2022-02-22 17:03:36 [INFO]\t[TRAIN] Epoch=16/30, Step=145/175, loss_xy=10.212868, loss_wh=3.323819, loss_obj=13.520772, loss_cls=1.534761, loss=28.592218, lr=0.000125, time_each_step=0.78s, eta=0:33:40\n",
      "2022-02-22 17:03:44 [INFO]\t[TRAIN] Epoch=16/30, Step=155/175, loss_xy=11.455891, loss_wh=2.852340, loss_obj=16.084843, loss_cls=1.651584, loss=32.044659, lr=0.000125, time_each_step=0.73s, eta=0:31:31\n",
      "2022-02-22 17:03:51 [INFO]\t[TRAIN] Epoch=16/30, Step=165/175, loss_xy=9.513961, loss_wh=2.187521, loss_obj=12.233788, loss_cls=1.324909, loss=25.260178, lr=0.000125, time_each_step=0.73s, eta=0:31:28\n",
      "2022-02-22 17:03:58 [INFO]\t[TRAIN] Epoch=16/30, Step=175/175, loss_xy=14.585277, loss_wh=4.510582, loss_obj=21.624491, loss_cls=3.493073, loss=44.213425, lr=0.000125, time_each_step=0.71s, eta=0:30:21\n",
      "2022-02-22 17:03:58 [INFO]\t[TRAIN] Epoch 16 finished, loss_xy=11.666886, loss_wh=3.1560822, loss_obj=15.589068, loss_cls=2.2842274, loss=32.696266 .\n",
      "2022-02-22 17:04:07 [INFO]\t[TRAIN] Epoch=17/30, Step=10/175, loss_xy=13.124496, loss_wh=3.129704, loss_obj=15.846409, loss_cls=3.032735, loss=35.133343, lr=0.000125, time_each_step=0.85s, eta=0:35:52\n",
      "2022-02-22 17:04:14 [INFO]\t[TRAIN] Epoch=17/30, Step=20/175, loss_xy=11.741207, loss_wh=3.021326, loss_obj=13.682969, loss_cls=1.763282, loss=30.208786, lr=0.000125, time_each_step=0.72s, eta=0:30:26\n",
      "2022-02-22 17:04:22 [INFO]\t[TRAIN] Epoch=17/30, Step=30/175, loss_xy=9.265152, loss_wh=2.222320, loss_obj=13.119078, loss_cls=1.147075, loss=25.753624, lr=0.000125, time_each_step=0.8s, eta=0:33:43\n",
      "2022-02-22 17:04:29 [INFO]\t[TRAIN] Epoch=17/30, Step=40/175, loss_xy=13.956064, loss_wh=4.083592, loss_obj=16.246899, loss_cls=3.167485, loss=37.454037, lr=0.000125, time_each_step=0.71s, eta=0:29:54\n",
      "2022-02-22 17:04:36 [INFO]\t[TRAIN] Epoch=17/30, Step=50/175, loss_xy=14.978432, loss_wh=4.169147, loss_obj=20.259323, loss_cls=2.998325, loss=42.405228, lr=0.000125, time_each_step=0.66s, eta=0:27:53\n",
      "2022-02-22 17:04:43 [INFO]\t[TRAIN] Epoch=17/30, Step=60/175, loss_xy=12.165495, loss_wh=3.467711, loss_obj=16.588528, loss_cls=3.346595, loss=35.568329, lr=0.000125, time_each_step=0.75s, eta=0:31:20\n",
      "2022-02-22 17:04:51 [INFO]\t[TRAIN] Epoch=17/30, Step=70/175, loss_xy=11.906048, loss_wh=2.603538, loss_obj=14.555765, loss_cls=1.484067, loss=30.549416, lr=0.000125, time_each_step=0.76s, eta=0:31:35\n",
      "2022-02-22 17:04:58 [INFO]\t[TRAIN] Epoch=17/30, Step=80/175, loss_xy=11.416453, loss_wh=2.928972, loss_obj=17.277718, loss_cls=1.915880, loss=33.539021, lr=0.000125, time_each_step=0.77s, eta=0:31:57\n",
      "2022-02-22 17:05:05 [INFO]\t[TRAIN] Epoch=17/30, Step=90/175, loss_xy=9.670498, loss_wh=2.220246, loss_obj=13.810880, loss_cls=2.234039, loss=27.935663, lr=0.000125, time_each_step=0.7s, eta=0:28:54\n",
      "2022-02-22 17:05:15 [INFO]\t[TRAIN] Epoch=17/30, Step=100/175, loss_xy=12.685940, loss_wh=4.465904, loss_obj=15.187437, loss_cls=1.860479, loss=34.199757, lr=0.000125, time_each_step=0.92s, eta=0:37:30\n",
      "2022-02-22 17:05:22 [INFO]\t[TRAIN] Epoch=17/30, Step=110/175, loss_xy=11.029169, loss_wh=2.720689, loss_obj=16.810642, loss_cls=2.160880, loss=32.721382, lr=0.000125, time_each_step=0.7s, eta=0:28:42\n",
      "2022-02-22 17:05:28 [INFO]\t[TRAIN] Epoch=17/30, Step=120/175, loss_xy=10.889991, loss_wh=2.968309, loss_obj=13.645557, loss_cls=1.951224, loss=29.455080, lr=0.000125, time_each_step=0.66s, eta=0:27:7\n",
      "2022-02-22 17:05:35 [INFO]\t[TRAIN] Epoch=17/30, Step=130/175, loss_xy=11.917330, loss_wh=3.503163, loss_obj=14.213764, loss_cls=3.550371, loss=33.184631, lr=0.000125, time_each_step=0.68s, eta=0:27:48\n",
      "2022-02-22 17:05:42 [INFO]\t[TRAIN] Epoch=17/30, Step=140/175, loss_xy=11.823258, loss_wh=2.968176, loss_obj=14.644907, loss_cls=2.268707, loss=31.705050, lr=0.000125, time_each_step=0.71s, eta=0:28:43\n",
      "2022-02-22 17:05:53 [INFO]\t[TRAIN] Epoch=17/30, Step=150/175, loss_xy=12.328535, loss_wh=2.784000, loss_obj=16.812241, loss_cls=2.138839, loss=34.063614, lr=0.000125, time_each_step=1.13s, eta=0:44:42\n",
      "2022-02-22 17:06:04 [INFO]\t[TRAIN] Epoch=17/30, Step=160/175, loss_xy=10.220186, loss_wh=2.530061, loss_obj=14.892025, loss_cls=1.931284, loss=29.573557, lr=0.000125, time_each_step=1.07s, eta=0:42:7\n",
      "2022-02-22 17:06:11 [INFO]\t[TRAIN] Epoch=17/30, Step=170/175, loss_xy=11.195986, loss_wh=2.578309, loss_obj=16.920004, loss_cls=2.773158, loss=33.467457, lr=0.000125, time_each_step=0.67s, eta=0:26:52\n",
      "2022-02-22 17:06:15 [INFO]\t[TRAIN] Epoch 17 finished, loss_xy=11.850146, loss_wh=3.1064463, loss_obj=15.65476, loss_cls=2.3327446, loss=32.944096 .\n",
      "2022-02-22 17:06:21 [INFO]\t[TRAIN] Epoch=18/30, Step=5/175, loss_xy=11.259058, loss_wh=3.200913, loss_obj=15.530091, loss_cls=2.364818, loss=32.354881, lr=0.000125, time_each_step=1.01s, eta=0:39:42\n",
      "2022-02-22 17:06:30 [INFO]\t[TRAIN] Epoch=18/30, Step=15/175, loss_xy=10.445654, loss_wh=2.391883, loss_obj=13.341860, loss_cls=1.582052, loss=27.761450, lr=0.000125, time_each_step=0.89s, eta=0:34:55\n",
      "2022-02-22 17:06:37 [INFO]\t[TRAIN] Epoch=18/30, Step=25/175, loss_xy=11.847485, loss_wh=3.684601, loss_obj=15.277338, loss_cls=2.376358, loss=33.185780, lr=0.000125, time_each_step=0.7s, eta=0:27:45\n",
      "2022-02-22 17:06:45 [INFO]\t[TRAIN] Epoch=18/30, Step=35/175, loss_xy=12.776297, loss_wh=3.526309, loss_obj=14.093345, loss_cls=2.196760, loss=32.592709, lr=0.000125, time_each_step=0.84s, eta=0:32:40\n",
      "2022-02-22 17:06:52 [INFO]\t[TRAIN] Epoch=18/30, Step=45/175, loss_xy=9.789278, loss_wh=2.087410, loss_obj=10.642893, loss_cls=1.296544, loss=23.816124, lr=0.000125, time_each_step=0.71s, eta=0:27:59\n",
      "2022-02-22 17:07:00 [INFO]\t[TRAIN] Epoch=18/30, Step=55/175, loss_xy=13.761066, loss_wh=3.006508, loss_obj=17.152878, loss_cls=1.990109, loss=35.910561, lr=0.000125, time_each_step=0.75s, eta=0:29:8\n",
      "2022-02-22 17:07:07 [INFO]\t[TRAIN] Epoch=18/30, Step=65/175, loss_xy=11.987156, loss_wh=3.044243, loss_obj=13.983882, loss_cls=3.966299, loss=32.981583, lr=0.000125, time_each_step=0.66s, eta=0:25:54\n",
      "2022-02-22 17:07:14 [INFO]\t[TRAIN] Epoch=18/30, Step=75/175, loss_xy=9.329217, loss_wh=2.131470, loss_obj=11.802479, loss_cls=1.078014, loss=24.341181, lr=0.000125, time_each_step=0.75s, eta=0:28:56\n",
      "2022-02-22 17:07:22 [INFO]\t[TRAIN] Epoch=18/30, Step=85/175, loss_xy=10.766325, loss_wh=2.808022, loss_obj=17.293839, loss_cls=2.398132, loss=33.266319, lr=0.000125, time_each_step=0.8s, eta=0:30:34\n",
      "2022-02-22 17:07:29 [INFO]\t[TRAIN] Epoch=18/30, Step=95/175, loss_xy=11.020504, loss_wh=2.659349, loss_obj=15.506847, loss_cls=2.496653, loss=31.683353, lr=0.000125, time_each_step=0.71s, eta=0:27:17\n",
      "2022-02-22 17:07:37 [INFO]\t[TRAIN] Epoch=18/30, Step=105/175, loss_xy=9.541399, loss_wh=2.919140, loss_obj=11.707134, loss_cls=2.103626, loss=26.271299, lr=0.000125, time_each_step=0.76s, eta=0:29:0\n",
      "2022-02-22 17:07:43 [INFO]\t[TRAIN] Epoch=18/30, Step=115/175, loss_xy=13.920366, loss_wh=3.650304, loss_obj=16.624043, loss_cls=2.690813, loss=36.885525, lr=0.000125, time_each_step=0.64s, eta=0:24:23\n",
      "2022-02-22 17:07:50 [INFO]\t[TRAIN] Epoch=18/30, Step=125/175, loss_xy=9.641812, loss_wh=2.975373, loss_obj=14.675355, loss_cls=1.703169, loss=28.995710, lr=0.000125, time_each_step=0.66s, eta=0:25:11\n",
      "2022-02-22 17:07:57 [INFO]\t[TRAIN] Epoch=18/30, Step=135/175, loss_xy=12.730018, loss_wh=4.249833, loss_obj=14.713392, loss_cls=2.595988, loss=34.289230, lr=0.000125, time_each_step=0.72s, eta=0:27:12\n",
      "2022-02-22 17:08:05 [INFO]\t[TRAIN] Epoch=18/30, Step=145/175, loss_xy=11.235185, loss_wh=3.492097, loss_obj=12.903024, loss_cls=3.553449, loss=31.183756, lr=0.000125, time_each_step=0.8s, eta=0:29:55\n",
      "2022-02-22 17:08:12 [INFO]\t[TRAIN] Epoch=18/30, Step=155/175, loss_xy=9.404552, loss_wh=2.449589, loss_obj=11.753448, loss_cls=1.576588, loss=25.184177, lr=0.000125, time_each_step=0.68s, eta=0:25:29\n",
      "2022-02-22 17:08:20 [INFO]\t[TRAIN] Epoch=18/30, Step=165/175, loss_xy=11.427531, loss_wh=2.790348, loss_obj=15.381426, loss_cls=1.991435, loss=31.590738, lr=0.000125, time_each_step=0.82s, eta=0:30:14\n",
      "2022-02-22 17:08:32 [INFO]\t[TRAIN] Epoch=18/30, Step=175/175, loss_xy=12.281031, loss_wh=3.333630, loss_obj=14.856411, loss_cls=1.556843, loss=32.027916, lr=0.000125, time_each_step=1.24s, eta=0:44:41\n",
      "2022-02-22 17:08:33 [INFO]\t[TRAIN] Epoch 18 finished, loss_xy=11.868588, loss_wh=3.123249, loss_obj=15.464273, loss_cls=2.336639, loss=32.79275 .\n",
      "2022-02-22 17:08:42 [INFO]\t[TRAIN] Epoch=19/30, Step=10/175, loss_xy=11.993924, loss_wh=3.132667, loss_obj=15.616440, loss_cls=2.908822, loss=33.651852, lr=0.000125, time_each_step=0.9s, eta=0:32:45\n",
      "2022-02-22 17:08:50 [INFO]\t[TRAIN] Epoch=19/30, Step=20/175, loss_xy=11.321171, loss_wh=3.813280, loss_obj=18.194393, loss_cls=2.813115, loss=36.141956, lr=0.000125, time_each_step=0.82s, eta=0:29:45\n",
      "2022-02-22 17:08:57 [INFO]\t[TRAIN] Epoch=19/30, Step=30/175, loss_xy=13.377350, loss_wh=3.061649, loss_obj=15.322557, loss_cls=1.598397, loss=33.359955, lr=0.000125, time_each_step=0.71s, eta=0:25:56\n",
      "2022-02-22 17:09:05 [INFO]\t[TRAIN] Epoch=19/30, Step=40/175, loss_xy=10.597213, loss_wh=2.771404, loss_obj=14.061150, loss_cls=1.615454, loss=29.045221, lr=0.000125, time_each_step=0.78s, eta=0:28:11\n",
      "2022-02-22 17:09:14 [INFO]\t[TRAIN] Epoch=19/30, Step=50/175, loss_xy=11.925879, loss_wh=3.132825, loss_obj=17.036327, loss_cls=3.018833, loss=35.113865, lr=0.000125, time_each_step=0.94s, eta=0:33:28\n",
      "2022-02-22 17:09:24 [INFO]\t[TRAIN] Epoch=19/30, Step=60/175, loss_xy=12.292486, loss_wh=3.405166, loss_obj=13.877929, loss_cls=3.823709, loss=33.399288, lr=0.000125, time_each_step=1.05s, eta=0:37:8\n",
      "2022-02-22 17:09:36 [INFO]\t[TRAIN] Epoch=19/30, Step=70/175, loss_xy=9.195885, loss_wh=2.289561, loss_obj=10.205946, loss_cls=1.622421, loss=23.313812, lr=0.000125, time_each_step=1.12s, eta=0:39:22\n",
      "2022-02-22 17:09:43 [INFO]\t[TRAIN] Epoch=19/30, Step=80/175, loss_xy=14.169812, loss_wh=2.973480, loss_obj=17.379419, loss_cls=1.811183, loss=36.333897, lr=0.000125, time_each_step=0.79s, eta=0:28:0\n",
      "2022-02-22 17:09:51 [INFO]\t[TRAIN] Epoch=19/30, Step=90/175, loss_xy=9.805696, loss_wh=2.653198, loss_obj=14.065976, loss_cls=2.105458, loss=28.630325, lr=0.000125, time_each_step=0.76s, eta=0:26:53\n",
      "2022-02-22 17:09:58 [INFO]\t[TRAIN] Epoch=19/30, Step=100/175, loss_xy=12.029455, loss_wh=3.007051, loss_obj=15.901446, loss_cls=1.482689, loss=32.420643, lr=0.000125, time_each_step=0.66s, eta=0:23:20\n",
      "2022-02-22 17:10:06 [INFO]\t[TRAIN] Epoch=19/30, Step=110/175, loss_xy=10.261206, loss_wh=2.299756, loss_obj=13.328615, loss_cls=1.646445, loss=27.536022, lr=0.000125, time_each_step=0.89s, eta=0:30:50\n",
      "2022-02-22 17:10:14 [INFO]\t[TRAIN] Epoch=19/30, Step=120/175, loss_xy=10.386265, loss_wh=2.689752, loss_obj=12.613878, loss_cls=1.558131, loss=27.248026, lr=0.000125, time_each_step=0.75s, eta=0:26:8\n",
      "2022-02-22 17:10:21 [INFO]\t[TRAIN] Epoch=19/30, Step=130/175, loss_xy=10.998663, loss_wh=3.198425, loss_obj=12.159362, loss_cls=1.922044, loss=28.278494, lr=0.000125, time_each_step=0.72s, eta=0:25:7\n",
      "2022-02-22 17:10:29 [INFO]\t[TRAIN] Epoch=19/30, Step=140/175, loss_xy=14.259443, loss_wh=3.887244, loss_obj=19.594604, loss_cls=3.364164, loss=41.105453, lr=0.000125, time_each_step=0.77s, eta=0:26:38\n",
      "2022-02-22 17:10:36 [INFO]\t[TRAIN] Epoch=19/30, Step=150/175, loss_xy=11.582376, loss_wh=2.544474, loss_obj=15.023184, loss_cls=2.192975, loss=31.343008, lr=0.000125, time_each_step=0.73s, eta=0:25:3\n",
      "2022-02-22 17:10:44 [INFO]\t[TRAIN] Epoch=19/30, Step=160/175, loss_xy=9.912643, loss_wh=2.414987, loss_obj=15.301229, loss_cls=2.206718, loss=29.835577, lr=0.000125, time_each_step=0.74s, eta=0:25:31\n",
      "2022-02-22 17:10:50 [INFO]\t[TRAIN] Epoch=19/30, Step=170/175, loss_xy=12.831274, loss_wh=2.886641, loss_obj=18.002037, loss_cls=2.764599, loss=36.484550, lr=0.000125, time_each_step=0.67s, eta=0:23:8\n",
      "2022-02-22 17:10:53 [INFO]\t[TRAIN] Epoch 19 finished, loss_xy=11.759824, loss_wh=3.0562758, loss_obj=15.283845, loss_cls=2.220082, loss=32.320026 .\n",
      "2022-02-22 17:10:58 [INFO]\t[TRAIN] Epoch=20/30, Step=5/175, loss_xy=13.491856, loss_wh=3.298822, loss_obj=17.098213, loss_cls=2.159390, loss=36.048283, lr=0.000125, time_each_step=0.74s, eta=0:24:46\n",
      "2022-02-22 17:11:05 [INFO]\t[TRAIN] Epoch=20/30, Step=15/175, loss_xy=13.631504, loss_wh=3.752573, loss_obj=18.600754, loss_cls=2.200508, loss=38.185341, lr=0.000125, time_each_step=0.73s, eta=0:24:4\n",
      "2022-02-22 17:11:13 [INFO]\t[TRAIN] Epoch=20/30, Step=25/175, loss_xy=9.373198, loss_wh=2.126257, loss_obj=11.333580, loss_cls=1.138682, loss=23.971716, lr=0.000125, time_each_step=0.81s, eta=0:26:35\n",
      "2022-02-22 17:11:20 [INFO]\t[TRAIN] Epoch=20/30, Step=35/175, loss_xy=12.656033, loss_wh=2.907994, loss_obj=15.011005, loss_cls=1.393660, loss=31.968691, lr=0.000125, time_each_step=0.68s, eta=0:22:19\n",
      "2022-02-22 17:11:27 [INFO]\t[TRAIN] Epoch=20/30, Step=45/175, loss_xy=10.086005, loss_wh=3.079604, loss_obj=13.054509, loss_cls=2.781759, loss=29.001879, lr=0.000125, time_each_step=0.74s, eta=0:24:15\n",
      "2022-02-22 17:11:35 [INFO]\t[TRAIN] Epoch=20/30, Step=55/175, loss_xy=11.422382, loss_wh=3.640189, loss_obj=17.212591, loss_cls=1.936320, loss=34.211483, lr=0.000125, time_each_step=0.8s, eta=0:25:55\n",
      "2022-02-22 17:11:45 [INFO]\t[TRAIN] Epoch=20/30, Step=65/175, loss_xy=11.762568, loss_wh=2.538714, loss_obj=13.144344, loss_cls=1.577500, loss=29.023125, lr=0.000125, time_each_step=0.99s, eta=0:31:45\n",
      "2022-02-22 17:11:55 [INFO]\t[TRAIN] Epoch=20/30, Step=75/175, loss_xy=14.415941, loss_wh=3.598086, loss_obj=18.947813, loss_cls=2.291301, loss=39.253139, lr=0.000125, time_each_step=0.99s, eta=0:31:30\n",
      "2022-02-22 17:12:03 [INFO]\t[TRAIN] Epoch=20/30, Step=85/175, loss_xy=14.395897, loss_wh=3.591089, loss_obj=16.418066, loss_cls=5.545373, loss=39.950424, lr=0.000125, time_each_step=0.75s, eta=0:23:56\n",
      "2022-02-22 17:12:10 [INFO]\t[TRAIN] Epoch=20/30, Step=95/175, loss_xy=12.040710, loss_wh=2.937881, loss_obj=17.717873, loss_cls=2.067293, loss=34.763756, lr=0.000125, time_each_step=0.68s, eta=0:21:36\n",
      "2022-02-22 17:12:16 [INFO]\t[TRAIN] Epoch=20/30, Step=105/175, loss_xy=9.367741, loss_wh=2.305090, loss_obj=12.001213, loss_cls=1.044715, loss=24.718758, lr=0.000125, time_each_step=0.66s, eta=0:21:2\n",
      "2022-02-22 17:12:23 [INFO]\t[TRAIN] Epoch=20/30, Step=115/175, loss_xy=12.100693, loss_wh=3.213824, loss_obj=16.158495, loss_cls=2.852006, loss=34.325016, lr=0.000125, time_each_step=0.73s, eta=0:22:54\n",
      "2022-02-22 17:12:31 [INFO]\t[TRAIN] Epoch=20/30, Step=125/175, loss_xy=12.229506, loss_wh=2.557933, loss_obj=16.402626, loss_cls=2.554555, loss=33.744621, lr=0.000125, time_each_step=0.78s, eta=0:24:28\n",
      "2022-02-22 17:12:39 [INFO]\t[TRAIN] Epoch=20/30, Step=135/175, loss_xy=9.003865, loss_wh=2.379766, loss_obj=13.371966, loss_cls=2.103899, loss=26.859495, lr=0.000125, time_each_step=0.74s, eta=0:22:59\n",
      "2022-02-22 17:12:46 [INFO]\t[TRAIN] Epoch=20/30, Step=145/175, loss_xy=9.278561, loss_wh=2.564317, loss_obj=14.072811, loss_cls=1.431061, loss=27.346748, lr=0.000125, time_each_step=0.74s, eta=0:22:58\n",
      "2022-02-22 17:12:52 [INFO]\t[TRAIN] Epoch=20/30, Step=155/175, loss_xy=11.882297, loss_wh=3.843984, loss_obj=14.157776, loss_cls=2.179214, loss=32.063271, lr=0.000125, time_each_step=0.63s, eta=0:19:26\n",
      "2022-02-22 17:12:59 [INFO]\t[TRAIN] Epoch=20/30, Step=165/175, loss_xy=10.449306, loss_wh=3.031494, loss_obj=13.001608, loss_cls=2.852500, loss=29.334908, lr=0.000125, time_each_step=0.65s, eta=0:19:57\n",
      "2022-02-22 17:13:07 [INFO]\t[TRAIN] Epoch=20/30, Step=175/175, loss_xy=11.205574, loss_wh=2.743332, loss_obj=14.443197, loss_cls=2.494763, loss=30.886866, lr=0.000125, time_each_step=0.82s, eta=0:24:54\n",
      "2022-02-22 17:13:07 [INFO]\t[TRAIN] Epoch 20 finished, loss_xy=11.703876, loss_wh=2.9985075, loss_obj=15.092653, loss_cls=2.1546633, loss=31.949701 .\n",
      "2022-02-22 17:13:07 [WARNING]\tDetector only supports single card evaluation with batch_size=1 during evaluation, so batch_size is forcibly set to 1.\n",
      "2022-02-22 17:13:08 [INFO]\tStart to evaluate(total_samples=1000, total_steps=1000)...\n",
      "2022-02-22 17:13:36 [INFO]\tAccumulating evaluatation results...\n",
      "2022-02-22 17:13:36 [INFO]\t[EVAL] Finished, Epoch=20, bbox_map=56.936637 .\n",
      "2022-02-22 17:13:38 [INFO]\tModel saved in output/yolov3_darknet53/best_model.\n",
      "2022-02-22 17:13:38 [INFO]\tCurrent evaluated best model on eval_dataset is epoch_20, bbox_map=56.93663727381636\n",
      "2022-02-22 17:13:39 [INFO]\tModel saved in output/yolov3_darknet53/epoch_20.\n",
      "2022-02-22 17:13:48 [INFO]\t[TRAIN] Epoch=21/30, Step=10/175, loss_xy=12.432615, loss_wh=2.821226, loss_obj=15.662493, loss_cls=2.426255, loss=33.342590, lr=0.000125, time_each_step=0.82s, eta=0:24:40\n",
      "2022-02-22 17:13:55 [INFO]\t[TRAIN] Epoch=21/30, Step=20/175, loss_xy=12.437637, loss_wh=2.874504, loss_obj=17.854561, loss_cls=1.619718, loss=34.786419, lr=0.000125, time_each_step=0.77s, eta=0:23:19\n",
      "2022-02-22 17:14:03 [INFO]\t[TRAIN] Epoch=21/30, Step=30/175, loss_xy=11.743441, loss_wh=3.018655, loss_obj=15.194801, loss_cls=2.443418, loss=32.400314, lr=0.000125, time_each_step=0.78s, eta=0:23:27\n",
      "2022-02-22 17:14:10 [INFO]\t[TRAIN] Epoch=21/30, Step=40/175, loss_xy=9.630968, loss_wh=2.307107, loss_obj=12.732574, loss_cls=1.499103, loss=26.169754, lr=0.000125, time_each_step=0.7s, eta=0:21:1\n",
      "2022-02-22 17:14:17 [INFO]\t[TRAIN] Epoch=21/30, Step=50/175, loss_xy=11.773378, loss_wh=2.686880, loss_obj=16.977024, loss_cls=2.452555, loss=33.889839, lr=0.000125, time_each_step=0.65s, eta=0:19:32\n",
      "2022-02-22 17:14:23 [INFO]\t[TRAIN] Epoch=21/30, Step=60/175, loss_xy=12.462629, loss_wh=3.301636, loss_obj=15.310006, loss_cls=1.544603, loss=32.618874, lr=0.000125, time_each_step=0.65s, eta=0:19:22\n",
      "2022-02-22 17:14:32 [INFO]\t[TRAIN] Epoch=21/30, Step=70/175, loss_xy=9.118133, loss_wh=2.144684, loss_obj=12.302735, loss_cls=1.272512, loss=24.838064, lr=0.000125, time_each_step=0.85s, eta=0:24:52\n",
      "2022-02-22 17:14:38 [INFO]\t[TRAIN] Epoch=21/30, Step=80/175, loss_xy=11.845277, loss_wh=3.253932, loss_obj=17.242834, loss_cls=3.389251, loss=35.731293, lr=0.000125, time_each_step=0.68s, eta=0:19:54\n",
      "2022-02-22 17:14:47 [INFO]\t[TRAIN] Epoch=21/30, Step=90/175, loss_xy=10.942644, loss_wh=2.621586, loss_obj=13.565166, loss_cls=1.598448, loss=28.727844, lr=0.000125, time_each_step=0.81s, eta=0:23:28\n",
      "2022-02-22 17:14:54 [INFO]\t[TRAIN] Epoch=21/30, Step=100/175, loss_xy=13.082552, loss_wh=3.003616, loss_obj=16.932446, loss_cls=2.790826, loss=35.809441, lr=0.000125, time_each_step=0.71s, eta=0:20:25\n",
      "2022-02-22 17:15:00 [INFO]\t[TRAIN] Epoch=21/30, Step=110/175, loss_xy=12.047766, loss_wh=2.964978, loss_obj=15.107893, loss_cls=2.491987, loss=32.612621, lr=0.000125, time_each_step=0.67s, eta=0:19:12\n",
      "2022-02-22 17:15:07 [INFO]\t[TRAIN] Epoch=21/30, Step=120/175, loss_xy=10.193350, loss_wh=2.882699, loss_obj=12.313375, loss_cls=1.711365, loss=27.100788, lr=0.000125, time_each_step=0.72s, eta=0:20:32\n",
      "2022-02-22 17:15:14 [INFO]\t[TRAIN] Epoch=21/30, Step=130/175, loss_xy=10.065304, loss_wh=3.057634, loss_obj=13.924618, loss_cls=1.115281, loss=28.162836, lr=0.000125, time_each_step=0.67s, eta=0:19:0\n",
      "2022-02-22 17:15:22 [INFO]\t[TRAIN] Epoch=21/30, Step=140/175, loss_xy=12.883416, loss_wh=3.239312, loss_obj=19.137144, loss_cls=3.682751, loss=38.942623, lr=0.000125, time_each_step=0.75s, eta=0:21:5\n",
      "2022-02-22 17:15:28 [INFO]\t[TRAIN] Epoch=21/30, Step=150/175, loss_xy=10.057206, loss_wh=2.788336, loss_obj=12.747655, loss_cls=2.720792, loss=28.313990, lr=0.000125, time_each_step=0.69s, eta=0:19:23\n",
      "2022-02-22 17:15:36 [INFO]\t[TRAIN] Epoch=21/30, Step=160/175, loss_xy=9.380986, loss_wh=2.436043, loss_obj=13.185801, loss_cls=1.663879, loss=26.666710, lr=0.000125, time_each_step=0.71s, eta=0:19:53\n",
      "2022-02-22 17:15:43 [INFO]\t[TRAIN] Epoch=21/30, Step=170/175, loss_xy=10.838105, loss_wh=3.144060, loss_obj=16.215569, loss_cls=1.485940, loss=31.683672, lr=0.000125, time_each_step=0.76s, eta=0:20:57\n",
      "2022-02-22 17:15:46 [INFO]\t[TRAIN] Epoch 21 finished, loss_xy=11.689489, loss_wh=2.9841857, loss_obj=14.841764, loss_cls=2.0964184, loss=31.611858 .\n",
      "2022-02-22 17:15:51 [INFO]\t[TRAIN] Epoch=22/30, Step=5/175, loss_xy=11.870898, loss_wh=3.425547, loss_obj=15.355816, loss_cls=1.906737, loss=32.558998, lr=0.000125, time_each_step=0.79s, eta=0:21:36\n",
      "2022-02-22 17:15:59 [INFO]\t[TRAIN] Epoch=22/30, Step=15/175, loss_xy=10.538338, loss_wh=2.803295, loss_obj=16.298609, loss_cls=1.344797, loss=30.985039, lr=0.000125, time_each_step=0.74s, eta=0:20:9\n",
      "2022-02-22 17:16:05 [INFO]\t[TRAIN] Epoch=22/30, Step=25/175, loss_xy=8.672173, loss_wh=1.995272, loss_obj=11.312422, loss_cls=2.413166, loss=24.393032, lr=0.000125, time_each_step=0.68s, eta=0:18:33\n",
      "2022-02-22 17:16:12 [INFO]\t[TRAIN] Epoch=22/30, Step=35/175, loss_xy=11.747663, loss_wh=2.902524, loss_obj=14.306324, loss_cls=1.770796, loss=30.727306, lr=0.000125, time_each_step=0.67s, eta=0:18:12\n",
      "2022-02-22 17:16:19 [INFO]\t[TRAIN] Epoch=22/30, Step=45/175, loss_xy=11.972063, loss_wh=2.596507, loss_obj=14.242067, loss_cls=2.245272, loss=31.055910, lr=0.000125, time_each_step=0.69s, eta=0:18:37\n",
      "2022-02-22 17:16:26 [INFO]\t[TRAIN] Epoch=22/30, Step=55/175, loss_xy=12.296806, loss_wh=4.393266, loss_obj=15.676771, loss_cls=2.252180, loss=34.619026, lr=0.000125, time_each_step=0.71s, eta=0:18:54\n",
      "2022-02-22 17:16:34 [INFO]\t[TRAIN] Epoch=22/30, Step=65/175, loss_xy=13.075834, loss_wh=3.139681, loss_obj=17.545731, loss_cls=2.440372, loss=36.201618, lr=0.000125, time_each_step=0.81s, eta=0:21:22\n",
      "2022-02-22 17:16:41 [INFO]\t[TRAIN] Epoch=22/30, Step=75/175, loss_xy=10.718809, loss_wh=2.462066, loss_obj=12.143624, loss_cls=1.833261, loss=27.157763, lr=0.000125, time_each_step=0.73s, eta=0:19:12\n",
      "2022-02-22 17:16:49 [INFO]\t[TRAIN] Epoch=22/30, Step=85/175, loss_xy=13.499439, loss_wh=3.760673, loss_obj=17.953041, loss_cls=1.916265, loss=37.129417, lr=0.000125, time_each_step=0.76s, eta=0:19:55\n",
      "2022-02-22 17:16:56 [INFO]\t[TRAIN] Epoch=22/30, Step=95/175, loss_xy=8.490851, loss_wh=1.830742, loss_obj=9.382401, loss_cls=0.785378, loss=20.489372, lr=0.000125, time_each_step=0.7s, eta=0:18:22\n",
      "2022-02-22 17:17:04 [INFO]\t[TRAIN] Epoch=22/30, Step=105/175, loss_xy=12.597685, loss_wh=3.468088, loss_obj=17.246021, loss_cls=4.477045, loss=37.788837, lr=0.000125, time_each_step=0.79s, eta=0:20:18\n",
      "2022-02-22 17:17:12 [INFO]\t[TRAIN] Epoch=22/30, Step=115/175, loss_xy=9.922277, loss_wh=2.659252, loss_obj=14.290978, loss_cls=1.151781, loss=28.024290, lr=0.000125, time_each_step=0.78s, eta=0:20:4\n",
      "2022-02-22 17:17:19 [INFO]\t[TRAIN] Epoch=22/30, Step=125/175, loss_xy=10.994013, loss_wh=2.321169, loss_obj=11.764727, loss_cls=0.982674, loss=26.062582, lr=0.000125, time_each_step=0.76s, eta=0:19:23\n",
      "2022-02-22 17:17:27 [INFO]\t[TRAIN] Epoch=22/30, Step=135/175, loss_xy=9.110525, loss_wh=2.356366, loss_obj=13.135802, loss_cls=1.380618, loss=25.983309, lr=0.000125, time_each_step=0.78s, eta=0:19:41\n",
      "2022-02-22 17:17:34 [INFO]\t[TRAIN] Epoch=22/30, Step=145/175, loss_xy=12.361040, loss_wh=4.141113, loss_obj=14.440505, loss_cls=1.908215, loss=32.850872, lr=0.000125, time_each_step=0.7s, eta=0:17:47\n",
      "2022-02-22 17:17:41 [INFO]\t[TRAIN] Epoch=22/30, Step=155/175, loss_xy=12.490214, loss_wh=2.775342, loss_obj=14.828110, loss_cls=1.578105, loss=31.671772, lr=0.000125, time_each_step=0.69s, eta=0:17:26\n",
      "2022-02-22 17:17:48 [INFO]\t[TRAIN] Epoch=22/30, Step=165/175, loss_xy=9.329699, loss_wh=2.588414, loss_obj=10.483313, loss_cls=1.369330, loss=23.770754, lr=0.000125, time_each_step=0.71s, eta=0:17:39\n",
      "2022-02-22 17:17:58 [INFO]\t[TRAIN] Epoch=22/30, Step=175/175, loss_xy=9.972900, loss_wh=2.643308, loss_obj=14.137236, loss_cls=2.118998, loss=28.872442, lr=0.000125, time_each_step=1.03s, eta=0:24:58\n",
      "2022-02-22 17:17:58 [INFO]\t[TRAIN] Epoch 22 finished, loss_xy=11.520545, loss_wh=2.9575398, loss_obj=14.754258, loss_cls=2.1156037, loss=31.347944 .\n",
      "2022-02-22 17:18:08 [INFO]\t[TRAIN] Epoch=23/30, Step=10/175, loss_xy=15.086889, loss_wh=4.278478, loss_obj=18.012220, loss_cls=2.019139, loss=39.396725, lr=0.000125, time_each_step=0.99s, eta=0:24:3\n",
      "2022-02-22 17:18:15 [INFO]\t[TRAIN] Epoch=23/30, Step=20/175, loss_xy=11.099294, loss_wh=3.036211, loss_obj=13.874657, loss_cls=1.944182, loss=29.954344, lr=0.000125, time_each_step=0.63s, eta=0:15:31\n",
      "2022-02-22 17:18:22 [INFO]\t[TRAIN] Epoch=23/30, Step=30/175, loss_xy=13.653708, loss_wh=3.176434, loss_obj=16.046516, loss_cls=1.879538, loss=34.756195, lr=0.000125, time_each_step=0.69s, eta=0:16:47\n",
      "2022-02-22 17:18:29 [INFO]\t[TRAIN] Epoch=23/30, Step=40/175, loss_xy=10.396019, loss_wh=2.666553, loss_obj=13.210824, loss_cls=1.357206, loss=27.630602, lr=0.000125, time_each_step=0.78s, eta=0:18:37\n",
      "2022-02-22 17:18:36 [INFO]\t[TRAIN] Epoch=23/30, Step=50/175, loss_xy=11.979567, loss_wh=3.034821, loss_obj=17.622072, loss_cls=2.265038, loss=34.901497, lr=0.000125, time_each_step=0.64s, eta=0:15:23\n",
      "2022-02-22 17:18:43 [INFO]\t[TRAIN] Epoch=23/30, Step=60/175, loss_xy=11.642879, loss_wh=2.438135, loss_obj=12.783410, loss_cls=1.610962, loss=28.475388, lr=0.000125, time_each_step=0.69s, eta=0:16:27\n",
      "2022-02-22 17:18:50 [INFO]\t[TRAIN] Epoch=23/30, Step=70/175, loss_xy=12.770183, loss_wh=3.266480, loss_obj=17.730511, loss_cls=1.862997, loss=35.630173, lr=0.000125, time_each_step=0.73s, eta=0:17:11\n",
      "2022-02-22 17:18:57 [INFO]\t[TRAIN] Epoch=23/30, Step=80/175, loss_xy=10.234359, loss_wh=2.608744, loss_obj=14.299134, loss_cls=2.023687, loss=29.165926, lr=0.000125, time_each_step=0.67s, eta=0:15:47\n",
      "2022-02-22 17:19:05 [INFO]\t[TRAIN] Epoch=23/30, Step=90/175, loss_xy=12.262326, loss_wh=3.021064, loss_obj=14.856907, loss_cls=2.130341, loss=32.270638, lr=0.000125, time_each_step=0.84s, eta=0:19:18\n",
      "2022-02-22 17:19:14 [INFO]\t[TRAIN] Epoch=23/30, Step=100/175, loss_xy=13.035151, loss_wh=2.852481, loss_obj=17.981113, loss_cls=1.389395, loss=35.258141, lr=0.000125, time_each_step=0.87s, eta=0:19:52\n",
      "2022-02-22 17:19:21 [INFO]\t[TRAIN] Epoch=23/30, Step=110/175, loss_xy=12.790533, loss_wh=2.754798, loss_obj=18.301479, loss_cls=1.401057, loss=35.247868, lr=0.000125, time_each_step=0.68s, eta=0:15:32\n",
      "2022-02-22 17:19:26 [INFO]\t[TRAIN] Epoch=23/30, Step=120/175, loss_xy=11.968433, loss_wh=2.775821, loss_obj=16.426455, loss_cls=2.749925, loss=33.920631, lr=0.000125, time_each_step=0.6s, eta=0:13:43\n",
      "2022-02-22 17:19:34 [INFO]\t[TRAIN] Epoch=23/30, Step=130/175, loss_xy=12.317607, loss_wh=2.670482, loss_obj=15.299121, loss_cls=1.612635, loss=31.899845, lr=0.000125, time_each_step=0.76s, eta=0:17:2\n",
      "2022-02-22 17:19:42 [INFO]\t[TRAIN] Epoch=23/30, Step=140/175, loss_xy=14.799263, loss_wh=3.432501, loss_obj=15.116341, loss_cls=1.407437, loss=34.755543, lr=0.000125, time_each_step=0.82s, eta=0:18:17\n",
      "2022-02-22 17:19:48 [INFO]\t[TRAIN] Epoch=23/30, Step=150/175, loss_xy=9.975676, loss_wh=2.343886, loss_obj=14.264607, loss_cls=2.187997, loss=28.772167, lr=0.000125, time_each_step=0.6s, eta=0:13:29\n",
      "2022-02-22 17:19:55 [INFO]\t[TRAIN] Epoch=23/30, Step=160/175, loss_xy=13.791536, loss_wh=3.078033, loss_obj=17.847763, loss_cls=2.892573, loss=37.609905, lr=0.000125, time_each_step=0.72s, eta=0:15:50\n",
      "2022-02-22 17:20:02 [INFO]\t[TRAIN] Epoch=23/30, Step=170/175, loss_xy=10.198366, loss_wh=2.955574, loss_obj=14.319468, loss_cls=2.601553, loss=30.074961, lr=0.000125, time_each_step=0.64s, eta=0:14:12\n",
      "2022-02-22 17:20:05 [INFO]\t[TRAIN] Epoch 23 finished, loss_xy=11.734117, loss_wh=2.9260406, loss_obj=14.881442, loss_cls=2.1070087, loss=31.648611 .\n",
      "2022-02-22 17:20:10 [INFO]\t[TRAIN] Epoch=24/30, Step=5/175, loss_xy=9.595679, loss_wh=2.181850, loss_obj=11.549184, loss_cls=2.145658, loss=25.472372, lr=0.000125, time_each_step=0.83s, eta=0:17:48\n",
      "2022-02-22 17:20:17 [INFO]\t[TRAIN] Epoch=24/30, Step=15/175, loss_xy=10.869594, loss_wh=2.685469, loss_obj=11.820032, loss_cls=1.912127, loss=27.287222, lr=0.000125, time_each_step=0.72s, eta=0:15:35\n",
      "2022-02-22 17:20:25 [INFO]\t[TRAIN] Epoch=24/30, Step=25/175, loss_xy=11.075431, loss_wh=2.571907, loss_obj=12.154551, loss_cls=1.692200, loss=27.494087, lr=0.000125, time_each_step=0.72s, eta=0:15:27\n",
      "2022-02-22 17:20:32 [INFO]\t[TRAIN] Epoch=24/30, Step=35/175, loss_xy=11.814787, loss_wh=2.499820, loss_obj=14.242602, loss_cls=1.581688, loss=30.138897, lr=0.000125, time_each_step=0.73s, eta=0:15:35\n",
      "2022-02-22 17:20:40 [INFO]\t[TRAIN] Epoch=24/30, Step=45/175, loss_xy=10.176823, loss_wh=2.703544, loss_obj=13.418994, loss_cls=2.965886, loss=29.265247, lr=0.000125, time_each_step=0.8s, eta=0:16:41\n",
      "2022-02-22 17:20:47 [INFO]\t[TRAIN] Epoch=24/30, Step=55/175, loss_xy=11.069675, loss_wh=2.382539, loss_obj=14.753358, loss_cls=1.505987, loss=29.711561, lr=0.000125, time_each_step=0.69s, eta=0:14:26\n",
      "2022-02-22 17:20:54 [INFO]\t[TRAIN] Epoch=24/30, Step=65/175, loss_xy=11.653966, loss_wh=3.302521, loss_obj=14.319706, loss_cls=1.669353, loss=30.945545, lr=0.000125, time_each_step=0.73s, eta=0:15:3\n",
      "2022-02-22 17:21:02 [INFO]\t[TRAIN] Epoch=24/30, Step=75/175, loss_xy=8.997501, loss_wh=2.180606, loss_obj=13.245119, loss_cls=1.479931, loss=25.903158, lr=0.000125, time_each_step=0.81s, eta=0:16:38\n",
      "2022-02-22 17:21:09 [INFO]\t[TRAIN] Epoch=24/30, Step=85/175, loss_xy=12.376432, loss_wh=3.358403, loss_obj=15.396296, loss_cls=2.684351, loss=33.815483, lr=0.000125, time_each_step=0.67s, eta=0:13:42\n",
      "2022-02-22 17:21:16 [INFO]\t[TRAIN] Epoch=24/30, Step=95/175, loss_xy=12.280792, loss_wh=4.303580, loss_obj=15.481915, loss_cls=3.080570, loss=35.146854, lr=0.000125, time_each_step=0.71s, eta=0:14:22\n",
      "2022-02-22 17:21:24 [INFO]\t[TRAIN] Epoch=24/30, Step=105/175, loss_xy=13.236191, loss_wh=3.110154, loss_obj=15.880040, loss_cls=2.219996, loss=34.446381, lr=0.000125, time_each_step=0.77s, eta=0:15:25\n",
      "2022-02-22 17:21:31 [INFO]\t[TRAIN] Epoch=24/30, Step=115/175, loss_xy=12.466614, loss_wh=4.243583, loss_obj=13.547212, loss_cls=1.587056, loss=31.844463, lr=0.000125, time_each_step=0.7s, eta=0:14:1\n",
      "2022-02-22 17:21:38 [INFO]\t[TRAIN] Epoch=24/30, Step=125/175, loss_xy=11.158993, loss_wh=3.529423, loss_obj=12.660173, loss_cls=2.513418, loss=29.862007, lr=0.000125, time_each_step=0.72s, eta=0:14:15\n",
      "2022-02-22 17:21:45 [INFO]\t[TRAIN] Epoch=24/30, Step=135/175, loss_xy=15.282885, loss_wh=4.650018, loss_obj=20.725082, loss_cls=3.426367, loss=44.084354, lr=0.000125, time_each_step=0.67s, eta=0:13:13\n",
      "2022-02-22 17:21:51 [INFO]\t[TRAIN] Epoch=24/30, Step=145/175, loss_xy=10.692350, loss_wh=2.281248, loss_obj=11.807369, loss_cls=1.301290, loss=26.082256, lr=0.000125, time_each_step=0.67s, eta=0:13:2\n",
      "2022-02-22 17:21:59 [INFO]\t[TRAIN] Epoch=24/30, Step=155/175, loss_xy=12.657660, loss_wh=3.190684, loss_obj=18.072889, loss_cls=2.009723, loss=35.930958, lr=0.000125, time_each_step=0.8s, eta=0:15:17\n",
      "2022-02-22 17:22:06 [INFO]\t[TRAIN] Epoch=24/30, Step=165/175, loss_xy=14.008870, loss_wh=2.861320, loss_obj=18.781046, loss_cls=2.028260, loss=37.679497, lr=0.000125, time_each_step=0.7s, eta=0:13:19\n",
      "2022-02-22 17:22:14 [INFO]\t[TRAIN] Epoch=24/30, Step=175/175, loss_xy=11.697145, loss_wh=2.630823, loss_obj=15.846801, loss_cls=1.907130, loss=32.081898, lr=0.000125, time_each_step=0.76s, eta=0:14:20\n",
      "2022-02-22 17:22:14 [INFO]\t[TRAIN] Epoch 24 finished, loss_xy=11.799373, loss_wh=2.9512892, loss_obj=14.861401, loss_cls=2.1135595, loss=31.725622 .\n",
      "2022-02-22 17:22:23 [INFO]\t[TRAIN] Epoch=25/30, Step=10/175, loss_xy=10.489013, loss_wh=2.516742, loss_obj=11.227859, loss_cls=0.988276, loss=25.221891, lr=0.000125, time_each_step=0.91s, eta=0:16:19\n",
      "2022-02-22 17:22:31 [INFO]\t[TRAIN] Epoch=25/30, Step=20/175, loss_xy=8.333371, loss_wh=1.821403, loss_obj=11.665070, loss_cls=1.041210, loss=22.861053, lr=0.000125, time_each_step=0.75s, eta=0:13:26\n",
      "2022-02-22 17:22:37 [INFO]\t[TRAIN] Epoch=25/30, Step=30/175, loss_xy=11.524685, loss_wh=3.254000, loss_obj=13.454124, loss_cls=1.545968, loss=29.778778, lr=0.000125, time_each_step=0.67s, eta=0:11:51\n",
      "2022-02-22 17:22:45 [INFO]\t[TRAIN] Epoch=25/30, Step=40/175, loss_xy=14.162983, loss_wh=3.862369, loss_obj=17.004311, loss_cls=2.230274, loss=37.259937, lr=0.000125, time_each_step=0.76s, eta=0:13:15\n",
      "2022-02-22 17:22:52 [INFO]\t[TRAIN] Epoch=25/30, Step=50/175, loss_xy=14.769075, loss_wh=3.240670, loss_obj=18.959166, loss_cls=2.248358, loss=39.217270, lr=0.000125, time_each_step=0.7s, eta=0:12:6\n",
      "2022-02-22 17:22:59 [INFO]\t[TRAIN] Epoch=25/30, Step=60/175, loss_xy=12.137844, loss_wh=2.423338, loss_obj=13.785963, loss_cls=1.452306, loss=29.799450, lr=0.000125, time_each_step=0.66s, eta=0:11:25\n",
      "2022-02-22 17:23:07 [INFO]\t[TRAIN] Epoch=25/30, Step=70/175, loss_xy=14.037621, loss_wh=3.713393, loss_obj=18.176226, loss_cls=2.557132, loss=38.484371, lr=0.000125, time_each_step=0.81s, eta=0:13:46\n",
      "2022-02-22 17:23:15 [INFO]\t[TRAIN] Epoch=25/30, Step=80/175, loss_xy=14.032839, loss_wh=3.659302, loss_obj=17.455395, loss_cls=1.765556, loss=36.913094, lr=0.000125, time_each_step=0.81s, eta=0:13:37\n",
      "2022-02-22 17:23:22 [INFO]\t[TRAIN] Epoch=25/30, Step=90/175, loss_xy=11.886569, loss_wh=3.449838, loss_obj=17.199936, loss_cls=2.198243, loss=34.734585, lr=0.000125, time_each_step=0.67s, eta=0:11:16\n",
      "2022-02-22 17:23:28 [INFO]\t[TRAIN] Epoch=25/30, Step=100/175, loss_xy=13.424339, loss_wh=3.096333, loss_obj=14.280167, loss_cls=1.441143, loss=32.241982, lr=0.000125, time_each_step=0.67s, eta=0:11:5\n",
      "2022-02-22 17:23:36 [INFO]\t[TRAIN] Epoch=25/30, Step=110/175, loss_xy=10.784322, loss_wh=2.548537, loss_obj=14.180414, loss_cls=1.500705, loss=29.013977, lr=0.000125, time_each_step=0.73s, eta=0:12:1\n",
      "2022-02-22 17:23:43 [INFO]\t[TRAIN] Epoch=25/30, Step=120/175, loss_xy=8.482997, loss_wh=2.265459, loss_obj=10.165462, loss_cls=0.883207, loss=21.797125, lr=0.000125, time_each_step=0.79s, eta=0:12:42\n",
      "2022-02-22 17:23:51 [INFO]\t[TRAIN] Epoch=25/30, Step=130/175, loss_xy=9.236179, loss_wh=2.042607, loss_obj=12.581110, loss_cls=1.280737, loss=25.140635, lr=0.000125, time_each_step=0.72s, eta=0:11:33\n",
      "2022-02-22 17:23:56 [INFO]\t[TRAIN] Epoch=25/30, Step=140/175, loss_xy=11.959056, loss_wh=4.060009, loss_obj=13.928455, loss_cls=1.419374, loss=31.366896, lr=0.000125, time_each_step=0.57s, eta=0:9:11\n",
      "2022-02-22 17:24:04 [INFO]\t[TRAIN] Epoch=25/30, Step=150/175, loss_xy=10.534637, loss_wh=3.423778, loss_obj=13.562291, loss_cls=3.425277, loss=30.945984, lr=0.000125, time_each_step=0.72s, eta=0:11:20\n",
      "2022-02-22 17:24:12 [INFO]\t[TRAIN] Epoch=25/30, Step=160/175, loss_xy=11.124064, loss_wh=2.414821, loss_obj=12.922833, loss_cls=1.301781, loss=27.763500, lr=0.000125, time_each_step=0.8s, eta=0:12:24\n",
      "2022-02-22 17:24:19 [INFO]\t[TRAIN] Epoch=25/30, Step=170/175, loss_xy=9.460494, loss_wh=1.997347, loss_obj=12.176935, loss_cls=1.556395, loss=25.191172, lr=0.000125, time_each_step=0.74s, eta=0:11:19\n",
      "2022-02-22 17:24:22 [INFO]\t[TRAIN] Epoch 25 finished, loss_xy=11.575159, loss_wh=2.8901293, loss_obj=14.309803, loss_cls=1.8134464, loss=30.588537 .\n",
      "2022-02-22 17:24:22 [WARNING]\tDetector only supports single card evaluation with batch_size=1 during evaluation, so batch_size is forcibly set to 1.\n",
      "2022-02-22 17:24:22 [INFO]\tStart to evaluate(total_samples=1000, total_steps=1000)...\n",
      "2022-02-22 17:24:49 [INFO]\tAccumulating evaluatation results...\n",
      "2022-02-22 17:24:50 [INFO]\t[EVAL] Finished, Epoch=25, bbox_map=57.736306 .\n",
      "2022-02-22 17:24:51 [INFO]\tModel saved in output/yolov3_darknet53/best_model.\n",
      "2022-02-22 17:24:51 [INFO]\tCurrent evaluated best model on eval_dataset is epoch_25, bbox_map=57.73630615601094\n",
      "2022-02-22 17:24:53 [INFO]\tModel saved in output/yolov3_darknet53/epoch_25.\n",
      "2022-02-22 17:24:58 [INFO]\t[TRAIN] Epoch=26/30, Step=5/175, loss_xy=10.452898, loss_wh=2.749116, loss_obj=12.941271, loss_cls=1.240436, loss=27.383720, lr=0.000125, time_each_step=0.79s, eta=0:11:54\n",
      "2022-02-22 17:25:05 [INFO]\t[TRAIN] Epoch=26/30, Step=15/175, loss_xy=15.132227, loss_wh=4.585811, loss_obj=15.567704, loss_cls=3.939663, loss=39.225407, lr=0.000125, time_each_step=0.7s, eta=0:10:35\n",
      "2022-02-22 17:25:12 [INFO]\t[TRAIN] Epoch=26/30, Step=25/175, loss_xy=12.191687, loss_wh=2.848603, loss_obj=16.579468, loss_cls=1.738344, loss=33.358101, lr=0.000125, time_each_step=0.71s, eta=0:10:33\n",
      "2022-02-22 17:25:19 [INFO]\t[TRAIN] Epoch=26/30, Step=35/175, loss_xy=11.010133, loss_wh=4.177224, loss_obj=14.891994, loss_cls=2.364029, loss=32.443382, lr=0.000125, time_each_step=0.65s, eta=0:9:34\n",
      "2022-02-22 17:25:26 [INFO]\t[TRAIN] Epoch=26/30, Step=45/175, loss_xy=11.854795, loss_wh=2.228613, loss_obj=12.581654, loss_cls=0.855706, loss=27.520769, lr=0.000125, time_each_step=0.76s, eta=0:10:59\n",
      "2022-02-22 17:25:33 [INFO]\t[TRAIN] Epoch=26/30, Step=55/175, loss_xy=10.451788, loss_wh=2.175211, loss_obj=12.953836, loss_cls=1.994976, loss=27.575811, lr=0.000125, time_each_step=0.66s, eta=0:9:29\n",
      "2022-02-22 17:25:39 [INFO]\t[TRAIN] Epoch=26/30, Step=65/175, loss_xy=12.485762, loss_wh=3.444288, loss_obj=15.541746, loss_cls=3.347865, loss=34.819660, lr=0.000125, time_each_step=0.65s, eta=0:9:15\n",
      "2022-02-22 17:25:47 [INFO]\t[TRAIN] Epoch=26/30, Step=75/175, loss_xy=9.701326, loss_wh=2.437172, loss_obj=11.642958, loss_cls=1.240654, loss=25.022110, lr=0.000125, time_each_step=0.78s, eta=0:10:50\n",
      "2022-02-22 17:25:54 [INFO]\t[TRAIN] Epoch=26/30, Step=85/175, loss_xy=10.331519, loss_wh=1.961043, loss_obj=11.147987, loss_cls=1.272161, loss=24.712709, lr=0.000125, time_each_step=0.71s, eta=0:9:47\n",
      "2022-02-22 17:26:02 [INFO]\t[TRAIN] Epoch=26/30, Step=95/175, loss_xy=12.174108, loss_wh=2.441660, loss_obj=14.114414, loss_cls=1.172398, loss=29.902580, lr=0.000125, time_each_step=0.75s, eta=0:10:13\n",
      "2022-02-22 17:26:09 [INFO]\t[TRAIN] Epoch=26/30, Step=105/175, loss_xy=10.683973, loss_wh=2.251254, loss_obj=16.285395, loss_cls=1.956903, loss=31.177526, lr=0.000125, time_each_step=0.79s, eta=0:10:34\n",
      "2022-02-22 17:26:16 [INFO]\t[TRAIN] Epoch=26/30, Step=115/175, loss_xy=11.140495, loss_wh=2.408547, loss_obj=13.297206, loss_cls=2.167543, loss=29.013792, lr=0.000125, time_each_step=0.66s, eta=0:8:52\n",
      "2022-02-22 17:26:23 [INFO]\t[TRAIN] Epoch=26/30, Step=125/175, loss_xy=11.352122, loss_wh=2.683140, loss_obj=13.069679, loss_cls=1.880633, loss=28.985575, lr=0.000125, time_each_step=0.73s, eta=0:9:38\n",
      "2022-02-22 17:26:30 [INFO]\t[TRAIN] Epoch=26/30, Step=135/175, loss_xy=11.334212, loss_wh=3.370412, loss_obj=13.532025, loss_cls=1.572842, loss=29.809490, lr=0.000125, time_each_step=0.65s, eta=0:8:27\n",
      "2022-02-22 17:26:38 [INFO]\t[TRAIN] Epoch=26/30, Step=145/175, loss_xy=9.893136, loss_wh=2.759712, loss_obj=13.021488, loss_cls=1.963601, loss=27.637938, lr=0.000125, time_each_step=0.84s, eta=0:10:40\n",
      "2022-02-22 17:26:45 [INFO]\t[TRAIN] Epoch=26/30, Step=155/175, loss_xy=13.047486, loss_wh=2.870673, loss_obj=20.117477, loss_cls=3.696023, loss=39.731659, lr=0.000125, time_each_step=0.71s, eta=0:8:58\n",
      "2022-02-22 17:26:54 [INFO]\t[TRAIN] Epoch=26/30, Step=165/175, loss_xy=8.570665, loss_wh=1.816889, loss_obj=11.942537, loss_cls=1.200337, loss=23.530428, lr=0.000125, time_each_step=0.84s, eta=0:10:28\n",
      "2022-02-22 17:27:00 [INFO]\t[TRAIN] Epoch=26/30, Step=175/175, loss_xy=13.890846, loss_wh=3.269234, loss_obj=17.436768, loss_cls=1.634280, loss=36.231129, lr=0.000125, time_each_step=0.65s, eta=0:8:3\n",
      "2022-02-22 17:27:00 [INFO]\t[TRAIN] Epoch 26 finished, loss_xy=11.807933, loss_wh=2.9961426, loss_obj=14.823563, loss_cls=2.1297112, loss=31.757349 .\n",
      "2022-02-22 17:27:09 [INFO]\t[TRAIN] Epoch=27/30, Step=10/175, loss_xy=8.900508, loss_wh=2.448816, loss_obj=10.908302, loss_cls=2.490181, loss=24.747807, lr=0.000125, time_each_step=0.85s, eta=0:10:18\n",
      "2022-02-22 17:27:16 [INFO]\t[TRAIN] Epoch=27/30, Step=20/175, loss_xy=12.556163, loss_wh=3.376024, loss_obj=14.472357, loss_cls=2.324097, loss=32.728642, lr=0.000125, time_each_step=0.75s, eta=0:8:56\n",
      "2022-02-22 17:27:23 [INFO]\t[TRAIN] Epoch=27/30, Step=30/175, loss_xy=14.279355, loss_wh=3.496711, loss_obj=20.931770, loss_cls=3.279402, loss=41.987240, lr=0.000125, time_each_step=0.64s, eta=0:7:37\n",
      "2022-02-22 17:27:30 [INFO]\t[TRAIN] Epoch=27/30, Step=40/175, loss_xy=8.829998, loss_wh=1.906064, loss_obj=11.464434, loss_cls=0.969514, loss=23.170012, lr=0.000125, time_each_step=0.76s, eta=0:8:54\n",
      "2022-02-22 17:27:37 [INFO]\t[TRAIN] Epoch=27/30, Step=50/175, loss_xy=13.203261, loss_wh=3.290671, loss_obj=15.595095, loss_cls=1.305672, loss=33.394699, lr=0.000125, time_each_step=0.69s, eta=0:7:56\n",
      "2022-02-22 17:27:46 [INFO]\t[TRAIN] Epoch=27/30, Step=60/175, loss_xy=9.749980, loss_wh=3.348704, loss_obj=10.924526, loss_cls=2.893249, loss=26.916458, lr=0.000125, time_each_step=0.87s, eta=0:9:45\n",
      "2022-02-22 17:28:00 [INFO]\t[TRAIN] Epoch=27/30, Step=70/175, loss_xy=9.256577, loss_wh=1.956921, loss_obj=11.618062, loss_cls=1.190946, loss=24.022507, lr=0.000125, time_each_step=1.4s, eta=0:15:14\n",
      "2022-02-22 17:28:09 [INFO]\t[TRAIN] Epoch=27/30, Step=80/175, loss_xy=11.800190, loss_wh=2.828195, loss_obj=13.525239, loss_cls=1.383047, loss=29.536671, lr=0.000125, time_each_step=0.91s, eta=0:9:50\n",
      "2022-02-22 17:28:17 [INFO]\t[TRAIN] Epoch=27/30, Step=90/175, loss_xy=12.925445, loss_wh=2.655873, loss_obj=15.286679, loss_cls=0.868523, loss=31.736519, lr=0.000125, time_each_step=0.77s, eta=0:8:18\n",
      "2022-02-22 17:28:23 [INFO]\t[TRAIN] Epoch=27/30, Step=100/175, loss_xy=11.604191, loss_wh=3.053061, loss_obj=16.113949, loss_cls=2.530737, loss=33.301941, lr=0.000125, time_each_step=0.67s, eta=0:7:13\n",
      "2022-02-22 17:28:30 [INFO]\t[TRAIN] Epoch=27/30, Step=110/175, loss_xy=11.537718, loss_wh=3.418724, loss_obj=14.391880, loss_cls=1.720564, loss=31.068886, lr=0.000125, time_each_step=0.61s, eta=0:6:31\n",
      "2022-02-22 17:28:37 [INFO]\t[TRAIN] Epoch=27/30, Step=120/175, loss_xy=13.349648, loss_wh=2.898434, loss_obj=15.888855, loss_cls=2.175444, loss=34.312378, lr=0.000125, time_each_step=0.77s, eta=0:7:53\n",
      "2022-02-22 17:28:44 [INFO]\t[TRAIN] Epoch=27/30, Step=130/175, loss_xy=13.711533, loss_wh=3.149457, loss_obj=15.599016, loss_cls=2.656906, loss=35.116913, lr=0.000125, time_each_step=0.72s, eta=0:7:18\n",
      "2022-02-22 17:28:52 [INFO]\t[TRAIN] Epoch=27/30, Step=140/175, loss_xy=11.179683, loss_wh=2.738884, loss_obj=13.434646, loss_cls=1.898557, loss=29.251770, lr=0.000125, time_each_step=0.74s, eta=0:7:22\n",
      "2022-02-22 17:28:59 [INFO]\t[TRAIN] Epoch=27/30, Step=150/175, loss_xy=11.293321, loss_wh=3.169376, loss_obj=13.960159, loss_cls=2.624310, loss=31.047165, lr=0.000125, time_each_step=0.72s, eta=0:7:4\n",
      "2022-02-22 17:29:06 [INFO]\t[TRAIN] Epoch=27/30, Step=160/175, loss_xy=10.730632, loss_wh=2.348360, loss_obj=14.723295, loss_cls=1.749907, loss=29.552193, lr=0.000125, time_each_step=0.66s, eta=0:6:26\n",
      "2022-02-22 17:29:12 [INFO]\t[TRAIN] Epoch=27/30, Step=170/175, loss_xy=15.539875, loss_wh=3.538622, loss_obj=18.108177, loss_cls=3.092929, loss=40.279606, lr=0.000125, time_each_step=0.68s, eta=0:6:27\n",
      "2022-02-22 17:29:17 [INFO]\t[TRAIN] Epoch 27 finished, loss_xy=11.762963, loss_wh=2.9088306, loss_obj=14.587623, loss_cls=1.9993262, loss=31.25874 .\n",
      "2022-02-22 17:29:23 [INFO]\t[TRAIN] Epoch=28/30, Step=5/175, loss_xy=15.428317, loss_wh=3.104504, loss_obj=19.298639, loss_cls=1.659169, loss=39.490627, lr=0.000125, time_each_step=1.01s, eta=0:9:16\n",
      "2022-02-22 17:29:30 [INFO]\t[TRAIN] Epoch=28/30, Step=15/175, loss_xy=13.174545, loss_wh=3.140648, loss_obj=17.076727, loss_cls=2.736964, loss=36.128887, lr=0.000125, time_each_step=0.77s, eta=0:7:4\n",
      "2022-02-22 17:29:36 [INFO]\t[TRAIN] Epoch=28/30, Step=25/175, loss_xy=11.385937, loss_wh=2.702382, loss_obj=15.471489, loss_cls=1.433217, loss=30.993025, lr=0.000125, time_each_step=0.56s, eta=0:5:9\n",
      "2022-02-22 17:29:43 [INFO]\t[TRAIN] Epoch=28/30, Step=35/175, loss_xy=15.601799, loss_wh=3.653539, loss_obj=21.760981, loss_cls=4.285362, loss=45.301682, lr=0.000125, time_each_step=0.73s, eta=0:6:26\n",
      "2022-02-22 17:29:50 [INFO]\t[TRAIN] Epoch=28/30, Step=45/175, loss_xy=13.342922, loss_wh=2.838881, loss_obj=13.396083, loss_cls=2.406646, loss=31.984531, lr=0.000125, time_each_step=0.7s, eta=0:6:7\n",
      "2022-02-22 17:29:58 [INFO]\t[TRAIN] Epoch=28/30, Step=55/175, loss_xy=10.687565, loss_wh=2.302775, loss_obj=14.994769, loss_cls=1.906298, loss=29.891405, lr=0.000125, time_each_step=0.78s, eta=0:6:37\n",
      "2022-02-22 17:30:04 [INFO]\t[TRAIN] Epoch=28/30, Step=65/175, loss_xy=8.859632, loss_wh=1.878014, loss_obj=10.088863, loss_cls=1.208432, loss=22.034939, lr=0.000125, time_each_step=0.54s, eta=0:4:38\n",
      "2022-02-22 17:30:11 [INFO]\t[TRAIN] Epoch=28/30, Step=75/175, loss_xy=10.639372, loss_wh=2.166325, loss_obj=12.417253, loss_cls=1.587299, loss=26.810249, lr=0.000125, time_each_step=0.78s, eta=0:6:22\n",
      "2022-02-22 17:30:19 [INFO]\t[TRAIN] Epoch=28/30, Step=85/175, loss_xy=13.289232, loss_wh=2.924621, loss_obj=14.820803, loss_cls=2.159206, loss=33.193859, lr=0.000125, time_each_step=0.77s, eta=0:6:10\n",
      "2022-02-22 17:30:25 [INFO]\t[TRAIN] Epoch=28/30, Step=95/175, loss_xy=14.446478, loss_wh=3.572885, loss_obj=17.701960, loss_cls=2.086025, loss=37.807346, lr=0.000125, time_each_step=0.6s, eta=0:4:45\n",
      "2022-02-22 17:30:33 [INFO]\t[TRAIN] Epoch=28/30, Step=105/175, loss_xy=12.278456, loss_wh=2.797082, loss_obj=15.346349, loss_cls=2.193391, loss=32.615276, lr=0.000125, time_each_step=0.81s, eta=0:6:10\n",
      "2022-02-22 17:30:39 [INFO]\t[TRAIN] Epoch=28/30, Step=115/175, loss_xy=12.199062, loss_wh=2.843203, loss_obj=14.770029, loss_cls=2.508798, loss=32.321091, lr=0.000125, time_each_step=0.62s, eta=0:4:41\n",
      "2022-02-22 17:30:47 [INFO]\t[TRAIN] Epoch=28/30, Step=125/175, loss_xy=14.487829, loss_wh=3.753117, loss_obj=15.055133, loss_cls=2.152499, loss=35.448578, lr=0.000125, time_each_step=0.74s, eta=0:5:27\n",
      "2022-02-22 17:30:54 [INFO]\t[TRAIN] Epoch=28/30, Step=135/175, loss_xy=14.743637, loss_wh=3.608531, loss_obj=17.253174, loss_cls=1.375363, loss=36.980705, lr=0.000125, time_each_step=0.72s, eta=0:5:10\n",
      "2022-02-22 17:31:00 [INFO]\t[TRAIN] Epoch=28/30, Step=145/175, loss_xy=11.429039, loss_wh=2.613003, loss_obj=11.942902, loss_cls=1.665750, loss=27.650694, lr=0.000125, time_each_step=0.65s, eta=0:4:35\n",
      "2022-02-22 17:31:08 [INFO]\t[TRAIN] Epoch=28/30, Step=155/175, loss_xy=10.571091, loss_wh=2.879668, loss_obj=15.878819, loss_cls=0.812921, loss=30.142498, lr=0.000125, time_each_step=0.73s, eta=0:4:59\n",
      "2022-02-22 17:31:15 [INFO]\t[TRAIN] Epoch=28/30, Step=165/175, loss_xy=10.478899, loss_wh=2.169230, loss_obj=11.070653, loss_cls=0.888623, loss=24.607405, lr=0.000125, time_each_step=0.75s, eta=0:5:0\n",
      "2022-02-22 17:31:22 [INFO]\t[TRAIN] Epoch=28/30, Step=175/175, loss_xy=13.234497, loss_wh=3.010271, loss_obj=15.921438, loss_cls=2.163705, loss=34.329910, lr=0.000125, time_each_step=0.69s, eta=0:4:32\n",
      "2022-02-22 17:31:22 [INFO]\t[TRAIN] Epoch 28 finished, loss_xy=11.697322, loss_wh=2.8676832, loss_obj=14.397509, loss_cls=1.9139893, loss=30.876501 .\n",
      "2022-02-22 17:31:31 [INFO]\t[TRAIN] Epoch=29/30, Step=10/175, loss_xy=11.855715, loss_wh=3.315776, loss_obj=13.330469, loss_cls=1.689772, loss=30.191732, lr=0.000125, time_each_step=0.88s, eta=0:5:28\n",
      "2022-02-22 17:31:39 [INFO]\t[TRAIN] Epoch=29/30, Step=20/175, loss_xy=11.977106, loss_wh=3.427132, loss_obj=12.612715, loss_cls=3.631788, loss=31.648741, lr=0.000125, time_each_step=0.76s, eta=0:4:39\n",
      "2022-02-22 17:31:46 [INFO]\t[TRAIN] Epoch=29/30, Step=30/175, loss_xy=14.463785, loss_wh=3.822812, loss_obj=16.471380, loss_cls=1.402285, loss=36.160267, lr=0.000125, time_each_step=0.74s, eta=0:4:27\n",
      "2022-02-22 17:31:54 [INFO]\t[TRAIN] Epoch=29/30, Step=40/175, loss_xy=12.392580, loss_wh=2.652093, loss_obj=16.323572, loss_cls=1.570780, loss=32.939022, lr=0.000125, time_each_step=0.8s, eta=0:4:37\n",
      "2022-02-22 17:32:02 [INFO]\t[TRAIN] Epoch=29/30, Step=50/175, loss_xy=10.626621, loss_wh=2.893030, loss_obj=13.113472, loss_cls=1.418470, loss=28.051594, lr=0.000125, time_each_step=0.74s, eta=0:4:10\n",
      "2022-02-22 17:32:09 [INFO]\t[TRAIN] Epoch=29/30, Step=60/175, loss_xy=11.949062, loss_wh=2.943704, loss_obj=15.817700, loss_cls=1.958647, loss=32.669117, lr=0.000125, time_each_step=0.75s, eta=0:4:7\n",
      "2022-02-22 17:32:16 [INFO]\t[TRAIN] Epoch=29/30, Step=70/175, loss_xy=11.859100, loss_wh=2.743480, loss_obj=16.337460, loss_cls=1.432994, loss=32.373035, lr=0.000125, time_each_step=0.71s, eta=0:3:48\n",
      "2022-02-22 17:32:25 [INFO]\t[TRAIN] Epoch=29/30, Step=80/175, loss_xy=12.887077, loss_wh=3.460451, loss_obj=14.842098, loss_cls=1.685444, loss=32.875072, lr=0.000125, time_each_step=0.86s, eta=0:4:22\n",
      "2022-02-22 17:32:33 [INFO]\t[TRAIN] Epoch=29/30, Step=90/175, loss_xy=10.499947, loss_wh=3.245173, loss_obj=11.778428, loss_cls=1.090963, loss=26.614511, lr=0.000125, time_each_step=0.8s, eta=0:3:57\n",
      "2022-02-22 17:32:41 [INFO]\t[TRAIN] Epoch=29/30, Step=100/175, loss_xy=13.911928, loss_wh=3.873188, loss_obj=15.850088, loss_cls=2.534511, loss=36.169716, lr=0.000125, time_each_step=0.79s, eta=0:3:46\n",
      "2022-02-22 17:32:48 [INFO]\t[TRAIN] Epoch=29/30, Step=110/175, loss_xy=12.275944, loss_wh=2.977062, loss_obj=14.531487, loss_cls=1.301511, loss=31.086004, lr=0.000125, time_each_step=0.76s, eta=0:3:32\n",
      "2022-02-22 17:32:55 [INFO]\t[TRAIN] Epoch=29/30, Step=120/175, loss_xy=11.881670, loss_wh=2.518761, loss_obj=14.197626, loss_cls=1.960966, loss=30.559023, lr=0.000125, time_each_step=0.68s, eta=0:3:6\n",
      "2022-02-22 17:33:02 [INFO]\t[TRAIN] Epoch=29/30, Step=130/175, loss_xy=14.164835, loss_wh=3.006908, loss_obj=21.044485, loss_cls=1.853863, loss=40.070091, lr=0.000125, time_each_step=0.73s, eta=0:3:10\n",
      "2022-02-22 17:33:09 [INFO]\t[TRAIN] Epoch=29/30, Step=140/175, loss_xy=9.986443, loss_wh=2.110199, loss_obj=11.835346, loss_cls=1.135948, loss=25.067936, lr=0.000125, time_each_step=0.67s, eta=0:2:51\n",
      "2022-02-22 17:33:17 [INFO]\t[TRAIN] Epoch=29/30, Step=150/175, loss_xy=10.221817, loss_wh=2.831203, loss_obj=11.169847, loss_cls=1.442125, loss=25.664993, lr=0.000125, time_each_step=0.76s, eta=0:3:1\n",
      "2022-02-22 17:33:24 [INFO]\t[TRAIN] Epoch=29/30, Step=160/175, loss_xy=10.922022, loss_wh=2.284683, loss_obj=13.078771, loss_cls=1.626182, loss=27.911657, lr=0.000125, time_each_step=0.7s, eta=0:2:43\n",
      "2022-02-22 17:33:32 [INFO]\t[TRAIN] Epoch=29/30, Step=170/175, loss_xy=12.056965, loss_wh=3.632873, loss_obj=15.127383, loss_cls=1.476140, loss=32.293362, lr=0.000125, time_each_step=0.84s, eta=0:3:1\n",
      "2022-02-22 17:33:36 [INFO]\t[TRAIN] Epoch 29 finished, loss_xy=11.759184, loss_wh=2.866672, loss_obj=14.443342, loss_cls=1.8508981, loss=30.920097 .\n",
      "2022-02-22 17:33:42 [INFO]\t[TRAIN] Epoch=30/30, Step=5/175, loss_xy=13.369387, loss_wh=3.418164, loss_obj=17.755291, loss_cls=2.010041, loss=36.552879, lr=0.000125, time_each_step=0.97s, eta=0:2:44\n",
      "2022-02-22 17:33:48 [INFO]\t[TRAIN] Epoch=30/30, Step=15/175, loss_xy=10.357087, loss_wh=2.599349, loss_obj=13.915686, loss_cls=1.457008, loss=28.329130, lr=0.000125, time_each_step=0.59s, eta=0:1:33\n",
      "2022-02-22 17:33:55 [INFO]\t[TRAIN] Epoch=30/30, Step=25/175, loss_xy=14.646973, loss_wh=3.573480, loss_obj=17.883263, loss_cls=2.694778, loss=38.798492, lr=0.000125, time_each_step=0.72s, eta=0:1:48\n",
      "2022-02-22 17:34:03 [INFO]\t[TRAIN] Epoch=30/30, Step=35/175, loss_xy=10.642245, loss_wh=2.437779, loss_obj=13.797269, loss_cls=1.830735, loss=28.708027, lr=0.000125, time_each_step=0.76s, eta=0:1:46\n",
      "2022-02-22 17:34:10 [INFO]\t[TRAIN] Epoch=30/30, Step=45/175, loss_xy=12.778874, loss_wh=3.242442, loss_obj=16.741535, loss_cls=3.089408, loss=35.852261, lr=0.000125, time_each_step=0.69s, eta=0:1:29\n",
      "2022-02-22 17:34:17 [INFO]\t[TRAIN] Epoch=30/30, Step=55/175, loss_xy=11.820387, loss_wh=2.527951, loss_obj=14.114367, loss_cls=0.828748, loss=29.291454, lr=0.000125, time_each_step=0.72s, eta=0:1:25\n",
      "2022-02-22 17:34:25 [INFO]\t[TRAIN] Epoch=30/30, Step=65/175, loss_xy=11.196781, loss_wh=2.749968, loss_obj=11.800770, loss_cls=2.436020, loss=28.183538, lr=0.000125, time_each_step=0.76s, eta=0:1:23\n",
      "2022-02-22 17:34:32 [INFO]\t[TRAIN] Epoch=30/30, Step=75/175, loss_xy=10.271130, loss_wh=2.562270, loss_obj=10.721327, loss_cls=3.506917, loss=27.061642, lr=0.000125, time_each_step=0.73s, eta=0:1:12\n",
      "2022-02-22 17:34:40 [INFO]\t[TRAIN] Epoch=30/30, Step=85/175, loss_xy=8.047157, loss_wh=1.728322, loss_obj=11.419212, loss_cls=1.088899, loss=22.283590, lr=0.000125, time_each_step=0.86s, eta=0:1:17\n",
      "2022-02-22 17:34:47 [INFO]\t[TRAIN] Epoch=30/30, Step=95/175, loss_xy=11.885826, loss_wh=2.804526, loss_obj=13.754457, loss_cls=1.666021, loss=30.110830, lr=0.000125, time_each_step=0.67s, eta=0:0:53\n",
      "2022-02-22 17:34:55 [INFO]\t[TRAIN] Epoch=30/30, Step=105/175, loss_xy=12.443843, loss_wh=3.367574, loss_obj=16.159363, loss_cls=1.982593, loss=33.953373, lr=0.000125, time_each_step=0.78s, eta=0:0:54\n",
      "2022-02-22 17:35:03 [INFO]\t[TRAIN] Epoch=30/30, Step=115/175, loss_xy=9.881039, loss_wh=2.385079, loss_obj=13.188178, loss_cls=0.977303, loss=26.431599, lr=0.000125, time_each_step=0.83s, eta=0:0:49\n",
      "2022-02-22 17:35:10 [INFO]\t[TRAIN] Epoch=30/30, Step=125/175, loss_xy=12.180265, loss_wh=3.893956, loss_obj=14.632045, loss_cls=2.961804, loss=33.668072, lr=0.000125, time_each_step=0.64s, eta=0:0:32\n",
      "2022-02-22 17:35:17 [INFO]\t[TRAIN] Epoch=30/30, Step=135/175, loss_xy=8.204016, loss_wh=1.859883, loss_obj=11.026726, loss_cls=2.201937, loss=23.292562, lr=0.000125, time_each_step=0.76s, eta=0:0:30\n",
      "2022-02-22 17:35:25 [INFO]\t[TRAIN] Epoch=30/30, Step=145/175, loss_xy=11.008247, loss_wh=2.286433, loss_obj=14.455862, loss_cls=1.832519, loss=29.583061, lr=0.000125, time_each_step=0.81s, eta=0:0:24\n",
      "2022-02-22 17:35:32 [INFO]\t[TRAIN] Epoch=30/30, Step=155/175, loss_xy=8.560116, loss_wh=2.088460, loss_obj=12.003151, loss_cls=1.107702, loss=23.759428, lr=0.000125, time_each_step=0.63s, eta=0:0:12\n",
      "2022-02-22 17:35:39 [INFO]\t[TRAIN] Epoch=30/30, Step=165/175, loss_xy=12.694789, loss_wh=4.067436, loss_obj=15.367478, loss_cls=2.620367, loss=34.750069, lr=0.000125, time_each_step=0.7s, eta=0:0:7\n",
      "2022-02-22 17:35:46 [INFO]\t[TRAIN] Epoch=30/30, Step=175/175, loss_xy=11.248508, loss_wh=2.581383, loss_obj=14.682285, loss_cls=1.251792, loss=29.763969, lr=0.000125, time_each_step=0.73s, eta=0:0:0\n",
      "2022-02-22 17:35:46 [INFO]\t[TRAIN] Epoch 30 finished, loss_xy=11.660939, loss_wh=2.8393548, loss_obj=14.327211, loss_cls=1.9985886, loss=30.826094 .\n",
      "2022-02-22 17:35:46 [WARNING]\tDetector only supports single card evaluation with batch_size=1 during evaluation, so batch_size is forcibly set to 1.\n",
      "2022-02-22 17:35:46 [INFO]\tStart to evaluate(total_samples=1000, total_steps=1000)...\n",
      "2022-02-22 17:36:14 [INFO]\tAccumulating evaluatation results...\n",
      "2022-02-22 17:36:14 [INFO]\t[EVAL] Finished, Epoch=30, bbox_map=60.460430 .\n",
      "2022-02-22 17:36:17 [INFO]\tModel saved in output/yolov3_darknet53/best_model.\n",
      "2022-02-22 17:36:17 [INFO]\tCurrent evaluated best model on eval_dataset is epoch_30, bbox_map=60.46043002273124\n",
      "2022-02-22 17:36:18 [INFO]\tModel saved in output/yolov3_darknet53/epoch_30.\n"
     ]
    }
   ],
   "source": [
    "model.train(\n",
    "    num_epochs=30,\n",
    "    train_dataset=train_dataset,\n",
    "    train_batch_size=20,\n",
    "    eval_dataset=eval_dataset,\n",
    "    learning_rate=0.001 / 8,\n",
    "    warmup_steps=1000,\n",
    "    warmup_start_lr=0.0,\n",
    "    save_interval_epochs=5,\n",
    "    lr_decay_epochs=[216, 243],\n",
    "    save_dir='output/yolov3_darknet53')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<center>step次数 如 **图3** 所示：\n",
    "<center><img src=\"https://ai-studio-static-online.cdn.bcebos.com/d9f51b579bd24bfbb517fba3d9492bfedf88bb90609d4068a4ba82ffb9a10bb9\" width=\"70%\" height=\"60%\"></center>\n",
    "<center><br>图3：step次数 </br></center>\n",
    "<br></br>\n",
    "\n",
    "\n",
    "\n",
    "**Train samples: 3500, num_epochs=50(这里算1轮的50没乘), train_batch_size=20, Step=175**\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2022-02-20 11:45:50 [INFO]\tModel[YOLOv3] loaded.\n",
      "[{'category_id': 1, 'category': 'helmet', 'bbox': [219.2314910888672, 67.85906219482422, 40.06431579589844, 46.71452331542969], 'score': 0.9880556464195251}, {'category_id': 1, 'category': 'helmet', 'bbox': [370.7378845214844, 132.32089233398438, 33.49871826171875, 36.958831787109375], 'score': 0.9075238108634949}, {'category_id': 1, 'category': 'helmet', 'bbox': [287.16180419921875, 132.24415588378906, 36.34478759765625, 38.101318359375], 'score': 0.5806847810745239}, {'category_id': 1, 'category': 'helmet', 'bbox': [190.13345336914062, 95.6458511352539, 42.21429443359375, 35.475914001464844], 'score': 0.05387658253312111}, {'category_id': 2, 'category': 'person', 'bbox': [271.3753662109375, 146.70663452148438, 54.404052734375, 118.34365844726562], 'score': 0.01536149624735117}, {'category_id': 2, 'category': 'person', 'bbox': [163.5753936767578, 90.10127258300781, 96.08778381347656, 145.990234375], 'score': 0.01054970920085907}]\n",
      "2022-02-20 11:45:50 [INFO]\tThe visualized result is saved at ./output/yolov3_darknet53/visualize_hard_hat_workers10.png\n"
     ]
    }
   ],
   "source": [
    "import paddlex as pdx\n",
    "model = pdx.load_model('output/yolov3_darknet53/best_model')\n",
    "image_name = 'data/JPEGImages/hard_hat_workers10.png'\n",
    "\n",
    "result = model.predict(image_name)\n",
    "print(result)\n",
    "pdx.det.visualize(image_name, result, threshold=0.5, save_dir='./output/yolov3_darknet53')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Notebook版本选BML Codecolab 选择output-yolov3_darknet53-vdl_log--里面的XXXX.log文件 启动VisualDL**\n",
    "<center><img src=\"https://ai-studio-static-online.cdn.bcebos.com/ff79ecab80724f2f8ab1076babb21efcadb1e8ceb6614639a86261c601c61df7\" width=\"70%\" height=\"60%\"></center>\n",
    "<center><br>图4：调用可视化方法 </br></center>\n",
    "\n",
    "**训练验证图示如下**\n",
    "<center>iteration次数 如 **图3** 所示：\n",
    "<center><img src=\"https://ai-studio-static-online.cdn.bcebos.com/26b6bd8cc25b41d0a1d734a75cb24fc74cd88621794f46e986cbbeee128ec618\" width=\"90%\" height=\"90%\"></center>\n",
    "<center><br>图5：训练验证图 </br></center>\n",
    "<br></br>\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 五、可视化模型效果"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f3b4e01d510>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import cv2\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "plt.imshow(cv2.imread(\"output/yolov3_darknet53/visualize_hard_hat_workers10.png\"))"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "py35-paddle1.2.0"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  },
  "toc-autonumbering": false,
  "toc-showcode": false,
  "toc-showtags": true
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
